paper linguistic analysis

Analyzing texts and items regarding their linguistic features might be important for researchers to investigate the effects of the linguistic presentation as well as for practitioners to estimate the readability of a text or an item. The Linguistic Analyzer for Text and Item Characteristics (LATIC) is a software that enables users to analyze texts and items more efficiently. LATIC offers a multitude of features at three different reading levels and can be used for texts and items in four different languages: English, French, German, and Spanish. It is open source, free to use and designed to be user-friendly. In this study, we investigated LATIC’s performance: LATIC achieves highly accurate results, while being extremely time saving compared to human raters. While developing LATIC, the respective features are tested continuously to ensure a high accuracy of results in the future.

Citation: Cruz Neri N, Klückmann F, Retelsdorf J (2022) LATIC–A linguistic analyzer for text and item characteristics. PLoS ONE 17(11): e0277250. https://doi.org/10.1371/journal.pone.0277250

Editor: Sergio Consoli, European Commission, ITALY

Received: March 15, 2022; Accepted: October 22, 2022; Published: November 3, 2022

Copyright: © 2022 Cruz Neri et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: In our analyses, we worked with multiple data. First, we worked with the MULTEXT-East 4.0 corpus (Erjavec et al., 2010) and the TIGER corpus 2.2 (Brants et al., 2004) to investigate how accurate the parts of speech tagging is. Both corpora are freely available online under https://www.clarin.si/repository/xmlui/handle/11356/1043 and https://www.ims.uni-stuttgart.de/documents/ressourcen/korpora/tiger-corpus/download/start.html . Since the data was obtained by third parties, we cannot share the data due to copyright reasons. However, we confirm that other authors could access the data in the same way we did. We did not have any special access or privileges that other authors would not have. Second, we worked with lists containing the most frequent words in English and German to examine the accuracy of the syllable count. For testing the German version, we worked with a list provided by the Leipzig Corpora Collection (Goldhahn et al., 2012), which can e downloaded under https://wortschatz.uni-leipzig.de/de/download/German . Again, we cannot share this list due to copyright reasons, but we confirm that other authors could access the data in the same way we did. We did not have any special access or privileges that other authors would not have. For the English version, we created a list ourselves. https://en-syllables.latic.software/ . Third, we worked with ratings of human coders. These are openly available on https://rating-results.latic.software/ .

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In the last few decades, many tools have been developed to analyze text and item characteristics regarding their linguistic and syntactical complexity. To name but a few, the tools TAACO (Tool for the Automatic Analysis of Cohesion) [ 1 ] and ReaderBench [ 2 ] are available for English texts and items. However, tools to date all have their own strengths, but might also have a few shortcomings regarding their features and accessibility for researchers and practitioners. For instance, some tools only allow the analysis of texts and items in one language (e.g., ARTE [Automatic Readability Tool for English] [ 3 ]) or fail to report accuracy measures of the implemented features (e.g., RATTE [Regensburg Analysis Tool for Texts] [ 4 ]).

This article introduces a new tool that tries to overcome some of these shortcomings: A Linguistic Analyzer for Text and Item Characteristics (LATIC). LATIC enables users to analyze texts and items automatically at three reading levels: the word, sentence, and text level. Using the Stanford CoreNLP [ 5 ], LATIC annotates parts of speech (e.g., nouns, verbs) and counts them. Additionally, LATIC can calculate different traditional readability indices (e.g., Flesch Reading Ease [ 6 ]; Läsbarhetsindex [LIX] [ 7 ]) and other objective measures (e.g., average sentence length, word count). To date, LATIC is available for English, French, German, and Spanish and can be used on most operating systems without a registration or even an installation being needed.

In this article, we aim to introduce LATIC and highlight its features. First, we illustrate why it is important to analyze linguistic text and item characteristics and how LATIC can help users in doing so. Therefore, we show how LATIC can be used and which features LATIC (version 1.2.2) provides to date. Second, we investigate LATIC’s performance in terms of accuracy and time savings (compared to human raters). Finally, in the discussion we elaborate on advantages as well as limitations of LATIC comparing it to other tools.

The importance of analyzing linguistic text and item characteristics

It is widely known that reading processes depend on reader characteristics, such as reading skills [ 8 – 10 ], but also on text and item characteristics [ 11 , 12 ]. Regarding the latter, especially the linguistic complexity has gained a lot of attention in the past decades [ 13 , 14 ]. In order to ensure that readers are able to comprehend written texts and items, it is essential to consider how linguistically complex these texts and items are. Many researchers and educators rely on traditional readability indices [ 15 ], such as the Flesch Reading Ease [ 6 ] and the SMOG (Simple Measure of Gobbledygook) [ 16 ] to estimate the complexity of a text or an item. However, it is argued that these readability indices might not be appropriate predictors of linguistic complexity since they are usually based on a few variables only, such as word and sentence length [ 17 ]. In fact, it is known that text and item characteristics that are not implemented into these readability indices can also generate difficulties in comprehension. The number of prepositional phrases, for instance, hinders comprehension and is associated with lower performance of students [ 18 ] and adults [ 12 ]. Hence, it is important not to rely on traditional readability indices only [ 17 ], but to analyze texts and items in more depth.

Many researchers draw on secondary analyses of test items and examine how specific text and item characteristics are linked to individuals’ performance on those test items [ 12 , 18 ]. For this, it is necessary to analyze said texts and items as a first step. Text and item characteristics can be manually tagged, coded and counted. For instance, Shaftel et al. [ 19 ] created a coding list for raters in order to rate test items regarding 17 characteristics. However, this procedure might be error prone and time consuming. Thus, there are many natural language processing (NLP; see [ 20 ] for a detailed description) tools that automatize this process in order to save resources [ 21 ]. There are many tools researchers and practitioners can choose from depending on the linguistic analyses they are aiming for. For instance, there are many (online) tools for parts of speech tagging, such as the online demo version of the Stanford CoreNLP [ 22 ]. In case users are interested in readability indices, they can opt for ARTE [ 3 ], when calculating English texts and items or RATTE [ 4 ], when analyzing German texts or items. Users are even confronted with more tools to choose from, when they want to analyze rather objective text and item characteristics, such as sentence length or word count (e.g., Coh-Metrix [ 23 ]; the SiNLP [Simple Natural Language Processing Tool] [ 21 ]).

However, it might be time consuming having to use several tools at once, which all might have their own strengths but also shortcomings. For instance, most tools are only available in one language [ 1 , 3 , 23 , 24 ]. More importantly, to the best of our knowledge, several developers provide no or only limited accuracy measures for the individual features implemented in their tools [ 4 , 21 ], which makes it difficult to decide whether the results are reliable or not. With LATIC, we aim to provide users with a tool that resolves some of the shortcomings other tools have. In the following, we introduce our tool and investigate how accurate LATIC’s results are compared to human ratings. In the discussion, we will further compare LATIC’s features with other tools, highlighting advantages as well as limitations of the tool.

The software LATIC

Technical details.

LATIC is a java application that is free and open source. LATIC can be used on Windows and Linux (e.g., Debian, Ubuntu) operating systems as well as on macOS to analyze texts and items. The duration of text and item analyses depends on the performance of the computer and the extent of the analyses. In our test runs, the analyses usually took a few seconds only.

For some features (i.e. tagging parts of speech), LATIC relies on a NLP tool. For LATIC, we used a NLP tool named Stanford CoreNLP [ 5 ]. The Stanford CoreNLP [ 25 ] is Java application providing users with several linguistic annotations for any kind of text, including parts of speech tagging. We always incorporate the latest version of the Stanford CoreNLP [ 5 ]. When submitting this manuscript, version 4.4.0 was the newest.

How to use LATIC.

LATIC is an intuitive and easy to use software that can be downloaded on https://download.latic.software . Users are able to use the software after installing LATIC (a step-by-step instruction is provided with the download). However, if users do not want to install LATIC, they can start it via the console. With using LATIC, data security is guaranteed since all analyses are performed on users’ local computers.

Fig 1 depicts the user interface of LATIC (version 1.2.2). In order to analyze a text or an item, users first need to choose the language of the text or item on the upper right. Second, users must enter the text or item in the text field or upload a document containing the text or item. Third, the users choose the characteristics that shall be analyzed on the right. Finally, the users need to click on the “Analyze” button to display the results. This step might take a few seconds. If users want to save their results, they can do so by clicking on the “Save file” button and choosing an appropriate file format, such as.csv files. The “Delete” button enables users to delete the results. A more detailed example is uploaded on GitHub [ 26 ]: It demonstrates the analysis of a famous tale and includes step-by-step instructions (including screenshots) to obtain the results.

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https://doi.org/10.1371/journal.pone.0277250.g001

If users need help, they can click on the “Help” button on the top row. They are provided with the option to write an e-mail or to retrieve the documentation of the software [ 27 ], including step-by-step instructions and detailed information about every feature of LATIC (see next section).

In order to ensure correct results, users need to keep a few factors in mind. First, similar to other software, it is essential to use correct spelling for reliable results, especially with regard to the parts of speech tagging. Second, abbreviations and measuring units should be avoided since they tend to bias both the tagging and the calculation of item characteristics (e.g., readability indices, average word length). Third, cardinal numbers should be written as Arabic numbers if tagging parts of speech is relevant for the analysis of the item. To calculate other characteristics, such as the syllable count or a readability index, the cardinal numbers should be spelled out. Finally, it is important to note that the Stanford CoreNLP [ 5 ] tags every juxtaposition of characters separated by spaces or punctuation marks individually. Hence, for instance, Red Sea would be counted as two words, each getting the tag of a proper noun.

LATIC allows users to analyze texts and items regarding their characteristics at different levels, i.e., the word, sentence and text level. Table 1 includes all features of LATIC. Due to the amount of features that LATIC provides, going into detail for every single features, would go beyond the scope of this paper. In the documentation of LATIC [ 27 ], we present every single feature of LATIC in much more detail, including short explanations and examples.

https://doi.org/10.1371/journal.pone.0277250.t001

First, LATIC enables the users to tag and count different parts of speech contained in a text or an item. The tagging of parts of speech is mostly based on the Stanford CoreNLP [ 5 ]. The only exceptions are the taggings of primary interjections and symbols in the French, German, and Spanish versions due to frequent errors in our test runs; these taggings are made by LATIC. The annotation in English is based on the Penn Treebank Tagset [ 28 ]; for French, German and Spanish the UD (version 2) tagset is used [ 29 ].

Furthermore, LATIC counts up the tags, thus providing the user with an absolute frequency of the parts of speech chosen for analysis. LATIC is also able to display the tags for every word or rather character juxtaposition. This feature enables the user to examine the tags given out by the Stanford CoreNLP [ 5 ].

Second, LATIC enables the user to analyze texts and items regarding other objective characteristics at all three levels, such as the average word and sentence length (see Table 1 ).

Third, LATIC’s features include the calculation of several readability indices (see Table 1 ). As noted above, traditional readability indices are heavily criticized [ 17 ] and we mostly agree with the critique. However, since the calculation of readability indices can be helpful for some research questions [ 15 ], we want to enable users to calculate them more easily. One must note that not every readability index is available in all languages because readability indices are not necessarily transferrable to other languages [ 30 ]. For instance, the Flesch Reading Ease [ 6 ] and the SMOG [ 16 ] for English texts and items needed to be adapted for the German language, resulting in the Flesch index [ 31 ] and the gSMOG [ 32 ]. Hence, we only implemented readability indices suitable for the respective languages.

The present study

In this study, we aim to first examine the performance of LATIC in terms of accuracy as well as time saving compared to analyses performed by human raters. For this, we aimed to focus on three aspects. During the development of LATIC, the implemented features were repeatedly tested and bugs were fixed immediately when noticed. However, there were two exceptions: The part of speech tagging by means of the Stanford CoreNLP 4.4.0 [ 5 ] as well as the syllable count. No errors occurred during our multiple test runs regarding all other features (such the counting of parts of speech and the calculation of text and item characteristics). Thus, we particularly wanted to investigate the accuracy of (1) the parts of speech tagging and (2) the syllable count. Furthermore, we aimed to investigate how the performance of LATIC compares to humans’ performance in terms of accuracy as well as time. Examining these aspects, we focus on the English and German language.

Parts of speech tagging

In order to test the accuracy of the tagging provided by the Stanford CoreNLP 4.4.0 [ 5 ], we compared the tokens given out by LATIC with the tokens of two well-known corpora (see [ 27 ] for more details). For the English language, we used the MULTEXT-East 4.0 corpus [ 33 ]. The corpus was manually tagged and consists of the book “1984” by George Orwell [ 34 ]. For the German language, we used the TIGER corpus 2.2 [ 35 ]. The corpus was tagged semi-automatically and consists of newspaper articles [ 36 ].

For evaluating the accuracy, we used around 10,000 tokens of each corpus. These tokens were analyzed with LATIC. However, to compare the accuracy of LATIC in a reasonable way, we needed to take a few steps. First, we deleted all words that were misspelled since a right spelling of words is essential for tagging the parts of speech correctly. Second, we matched the respective tagsets to the ones used by the Stanford CoreNLP 4.4.0 [ 5 ]. Pronouns, for instance, are tagged in much more detail in the MULTEXT-East 4.0 corpus [ 37 ] than in the tagsets used by the Stanford CoreNLP [ 5 ]. Third, we examined how many tokens were tagged correctly by LATIC by means of the Stanford CoreNLP 4.4.0 [ 5 ].

Calculation of syllable count

In the English language, we used a script [ 38 ] to count the syllables. First, we tested the script with N = 9,107 common words ( https://en-syllables.latic.software/ ). In a second step, we optimized the results by adapting the script: We created a list of words that were counted incorrectly and implemented the correct syllable count for each of those words [ 39 ].

In the German language, we created an algorithm to count the syllables. First, we tested this algorithm with N = 9,524 common words according to the Leipzig Corpora Collection [ 40 ]. Again, in a second step, we optimized the algorithm by including (1) additional rules to correctly count the syllables, (2) a list of words, in which the syllables were not correctly counted, and (3) Anglicisms and Gallicisms that are common in German.

Comparison with human performance

We chose five openly available science items that were used in the PISA study (Programme for International Student Assessment) in 2015 [ 41 ]: (1) the introductory text of item S656, (2) S656Q01, (3) S656Q02 as well as (4) the introductory text of item S641, and (5) the item S641Q01. The chosen items ranged from 22 to 101 words and did not include any illustrations.

We asked four people to analyze the items regarding the selected text and item characteristics. The four raters were two student assistants with little experience in linguistic coding, one foreign language assistant with decent experience in linguistic coding as well as the first author of this paper who has a lot of experience in linguistic coding. The first three raters were given an instruction on how to analyze text and item characteristics by the first author. Especially the two student assistants received further explanations and support due to their little experience in coding.

We randomly selected thirteen text and item characteristics that the raters should analyze (see Table 2 ). The raters were asked to (1) give their ratings regarding the characteristics, (2) state how confident they were in their results (ranging from 1 [not at all] to 5 [very confident]), and (3) to measure how long it took for them to analyze each item.

https://doi.org/10.1371/journal.pone.0277250.t002

Ethical approval

An ethics committee approval was not needed due to solely working with entered text or items in LATIC. Sensitive data, such as from vulnerable people, were not used in developing the software or preparing this manuscript. All four raters of legal age gave their consent in participating in the study.

Regarding the plain comparison of tokens, LATIC correctly tagged n = 8,651 out of all N = 9,989 (86.61%) in the English language, and n = 9,093 out of all N = 9,997 (90.96%) in the German language. As noted above, some modifications needed to be made to ensure a reasonable comparison of the tokens. After these modifications, n = 9,048 out of the remaining N = 9,755 (92.75%) of the tokens were correctly tagged by the Stanford CoreNLP 4.4.0 [ 5 ] in the English language; in the German language, n = 9,271 out of the remaining N = 9,879 (93.85%) of the tokens were correct. Finally, we analyzed the most frequent errors in tagging by examining the first 5,000 tokens. In both languages, the differentiation between determiners and pronouns seemed to be the most challenging (English: 37.84% of all errors; German: 21.74% of all errors). However, this was to be expected since the differentiation between these two word classes is not always clear [ 29 ]. In the English language, the distinction of adjectives and the tagging of (1) prepositions and subordinating conjunctions (11.28% of all errors), and (2) all types of verbs (8.52% of all errors) were by far the second and third most frequent errors. In the German language, the second and third most frequent errors were the distinction between nouns and proper nouns (13.71% of all errors) and the distinction between adjectives and adverbs (9.36% of all errors).

In the English language, the syllables of n = 8,723 words (95.78%) were counted correctly. After adapting the script by Wormer (2021), the syllables of all N = 9,107 test words (100%) were correctly counted. In the German language, the syllable count of n = 9,190 words (96.49%) was correctly calculated. After optimizing our algorithm, the syllable count was correct for n = 9,522 test words (99.98%).

All results by the four raters as well as LATIC’s results are openly available on https://rating-results.latic.software/ . The means of the raters’ ratings as well as LATIC’s results are depicted in Table 2 . In terms of accuracy, the raters felt confident that their ratings were accurate ( M = 4.27, SD = 0.55). In fact, conducting a Mann-Whitney-U-test, there are no significant differences between the coders’ and LATIC’s results, U = 2081.50, p = .885. We also tested whether the overall ratings of each individual rater differed from LATIC’s results by conducting further Mann-Whitney-U-tests. All differences were non-significant ( p > .570).

Despite the differences not being significant, LATIC still achieves higher accuracy than the raters regarding certain text and item characteristics, such as calculating the lexical diversity [ 42 ] and the readability index LIX [ 7 ]. This can be mostly traced back to human raters’ calculation errors, which LATIC as a technical software does not make.

The interrater-reliability (Krippendorff’s α) [ 43 ] varies tremendously between the different text and item characteristics. It can be positively mentioned that the raters reach (very) good reliabilities (Krippendorff’s α ≥ 0.80) for more than half of the text and item characteristics (e.g., number of sentences, word count). However, it becomes apparent that not all ratings made by humans are suitable to work with since some interrater reliabilities are very low (e.g., pronouns, LIX).

In total, the four raters needed M = 4,374.80 seconds (~ 72 minutes; SD = 1,846.25) to analyze all five items regarding the thirteen text and item characteristics. For the analyses of each item, the four raters used M = 2.60 ( SD = 0.55) different tools, which they used M = 7.80 ( SD = 0.45) times. The first author further analyzed the five items by means of LATIC and finished the analyses within 80 seconds, including starting the software and choosing the text and item characteristics on the right sidebar (see section “How to Use LATIC”).

As noted above, there are a few tools and web applications used by researchers to automatize the process of analyzing text and item characteristics. However, these tools and applications might only be partially suitable for envisioned purposes. Thus, in this article, we introduced the new tool LATIC which combines many linguistic features, such as parts of speech tagging, the calculation of objective text and item characteristics as well as readability indices. On the basis of the Stanford CoreNLP [ 5 ], users can analyze texts and items in English, French, German, and Spanish. Drawing on our results, LATIC achieves very high performance in terms of accuracy as well as time saving compared to human raters. Thus, LATIC provides reliable results, while also saving users’ resources.

Strengths of LATIC

In research, it is important to work with reliable results in order to draw plausible and accurate conclusions. Thus, it is essential to work with tools and software that provide researchers with such reliable results. Unfortunately, to the best of our knowledge, many authors also fail to provide measurements of accuracy for their tools or rather for specific features of their tools (for example [ 4 ]). Our article shows that users can analyze linguistic text and item characteristics with high accuracy in a short time with LATIC. The tagging provided in LATIC by means of the Stanford CoreNLP 4.4.0 [ 5 ] reaches very good accuracy in our analyses, which corroborates prior research. In other evaluation studies, the Stanford CoreNLP [ 5 ] usually reaches one of the best accuracy rates compared to other parts of speech taggers [ 44 , 45 ]. Furthermore, the accuracy of the syllable count is also high, especially when adapting the script and algorithm to optimize the syllable count feature. Finally, comparing LATIC’s performance with human raters, the results of our analyses showed that LATIC reaches similar accuracy as human raters. At this point, it is important to note that the interrater-reliability varies tremendously for some linguistic text and item characteristics suggesting that a tool like LATIC might provide more adequate rating results than human raters. In sum, LATIC provides reliable results while saving the users valuable resources, such as time and effort, since the analyses usually only takes (milli-) seconds.

In terms of scientific aspects, many authors traditionally tend to manually tag, code and count text and item characteristics, such as word count [ 9 ] or prepositions [ 19 ]. While doing this, the human raters might fall back on several different tools, which was the case in our study. This might be due to some web applications offering only a limited number of features (e.g., T.E.R.A [ 46 ]) or even just one (e.g., LIX calculator [ 47 ]). LATIC provides the users with the advantage of combining many features in one software that are essential for evaluating the readability of texts and items. As noted above, LATIC as a tool offers users to automatically tag and count parts of speech. Furthermore, LATIC calculates further characteristics as well as traditional readability indices, which are regularly used in research [ 10 , 19 , 48 ].

In terms of breadth of features, most tools and applications only analyze texts or items in one specific language (e.g., ARTE [ 3 ]). LATIC offers the linguistic analyses of texts and items in four languages (English, French, German, and Spanish). Further languages can be implemented into LATIC, if suitable natural language processing tools are available. For instance, besides the languages already implemented into LATIC, the Stanford CoreNLP further supports tagging parts of speech in the Arabic, Hungarian, Italian, and Chinese languages [ 25 ]. However, since none of the authors is fluent in neither of those languages, these could not be implemented (yet).

Regarding usability aspects, some web applications are not always available online (Coh-Metrix [ 46 ]; Stanford CoreNLP demo [ 22 ]), require a registration (e.g., CTAP [ 49 ]; Text Inspector [ 24 ]), or are not free to use (e.g., LIWC [ 50 ]; Text Inspector [ 24 ]). LATIC is open source and free to use. Thus, the code is openly available [ 39 ] and can be audited and modified. Furthermore, users can request features to be implemented into LATIC and may report bugs under https://github.com/florianklueckmann/LATIC/issues or by sending an e-mail to [email protected] . It is also of advantage that the interface is user-friendly and intuitively usable, thus diminishing long training periods. All features are documented in detail [ 27 ], which provides users with essential information on how the results of LATIC are formed. LATIC also enables users to work with the results by allowing users to save and import the results into statistical analysis software, if needed.

Limitations

Despite the strengths of LATIC, a few limitations need to be discussed. First, in order to obtain the most reliable results possible, users need to follow a few instructions. Similar to other tools and software, this includes ensuring the correct spelling and avoiding abbreviations (see above). This means that users have a high responsibility to ensure the entered texts and items are examined and—if necessary—modified accordingly. This means that, for instance, texts written by students can only be analyzed regarding their characteristics, if the spelling is corrected beforehand. However, it might be the case that researchers or practitioners are especially interested in these errors. In this case, we would recommend using the software GAMET (Grammar and Mechanics Error Tool) [ 51 ]. The software enables users to locate structural as well as mechanical errors within an English text, which is the focal point of the tool. Parts of speech tagging or the calculation of text and item characteristics, however, cannot be executed.

Second, some linguistic characteristics prominent in readability research, such as cohesion [ 52 ] or phrases [ 18 ], cannot be analyzed in LATIC. However, there are a number of tools enabling users to analyze their English texts and items regarding cohesion. For instance, the tool TAACO [ 1 ] estimates over 150 indices of global and local cohesion. While TAACO provides many indices of cohesion, other text and item characteristics, such as readability indices or the syllable count, cannot be calculated. In case, users want to analyze phrases, we recommend the tool TAASSC (Tool for the Automatic Analysis of Syntactic Sophistication and Complexity) [ 53 , 54 ]. While TAASC focuses on estimating the syntactic sophistication as well as complexity of English texts, parts of speech tagging as well as the calculation of the syllable count and readability indices are not available.

Finally, when developing LATIC, we decided to implement the Stanford CoreNLP [ 5 ] rather than other NLP tools due to its high accuracy rates in prior research [ 44 , 45 ]. However, one must note that the Stanford CoreNLP [ 5 ] seems to perform relatively well on generic text and item types, but not in significantly different text types. For instance, other parts of speech taggers outperform the Stanford CoreNLP [ 5 ] in social media texts [ 55 ] or ReadMe documents of software [ 56 ]. The main intent was to provide educators and (readability) researchers the opportunity to analyze linguistic text and item characteristics with LATIC. Thus, we opted for an NLP tool that performed well on rather formal text and item types found, for instance, in the educational context.

The analyses of linguistic text and item characteristics might not only be necessary for investigating research questions regarding language in texts and items, but also for practitioners, such as teachers and educators. Traditionally, texts and items are analyzed manually [ 9 , 19 ], which is very time consuming. LATIC is a user friendly and free to use java application enabling users to analyze texts and items in four languages (English, French, German, and Spanish) regarding their linguistic characteristics. LATIC offers features at the word, sentence and text level including the tagging and counting of parts of speech and the calculating of traditional readability indices. All features were tested thoroughly and reach remarkable accuracy.

Acknowledgments

We would like to thank Judith Keinath for the editorial assistance with this article.

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Language Used in Academic Writing
MHRA Referencing
Opinion vs Fact
Works Cited
Emotional Arguments in Essays
Ethical Arguments in Essays
Logical Arguments in Essays
The Argument
Writing an Argumentative Essay
Image Caption
Microblogging
Personal Blog
Professional Blog
Syntactical
Anaphoric Reference
Backchannels
Cataphoric Reference
Discourse Analysis
Discourse Markers
Endophoric Reference
Exophoric Reference
Interruption
John Swales Discourse Communities
Turn-taking
Email Closings
Email Introduction
Email Salutation
Email Signature
Email Subject Lines
Formal Email
Informal Email
Active Voice
Adjective Phrase
Adverb Phrase
Complex Sentence
Compound Adjectives
Compound Sentence
Conditional Sentences
Conjugation
Conjunction
Coordinating Conjunctions
Copula Verbs
Correlative Conjunctions
Dangling Participle
Declaratives
Demonstrative Pronouns
Dependent Clause
Descriptive Adjectives
Exclamatives
Future Tense
Grammatical Mood
Grammatical Voices
Imperative Mood
Imperatives
Indefinite Pronouns
Independent Clause
Indicative Mood
Infinitive Mood
Interjections
Interrogative Mood
Interrogatives
Irregular Verbs
Linking Verb
Misplaced Modifiers
Modal Verbs
Noun Phrase
Optative Mood
Passive Voice
Past Perfect Tense
Perfect Aspect
Personal Pronouns
Possessive Pronouns
Potential Mood
Preposition
Prepositional Phrase
Present Participle
Present Perfect Progressive
Present Perfect Tense
Present Tense
Progressive Aspect
Proper Adjectives
Reflexive Pronouns
Relative Pronouns
Sentence Functions
Simple Sentence
Subjunctive Mood
Subordinating Conjunctions
Superlative Adjectives
Transitive and Intransitive Verbs
Types of Phrases
Types of Sentence
Verb Phrase
Academic English
Anglo Saxon Roots and Prefixes
Bilingual Dictionaries
Contractions
English Dictionaries
English Vocabulary
Greek Roots, Suffixes and Prefixes
Latin Roots, Suffixes and Prefixes
Modern English
Object category
Parentheses
Possessives
Regional Dialects
Rhyming Dictionary
Sentence Fragments
Social Dialects
Subject Predicate Relationship
Subject Verb Agreement
Word Pronunciation
Essay Time Management
How To Take a Position in an Essay
Organize Your Prompt
Proofread Essay
Understanding the Prompt
Analytical Essay
Cause and Effect Essay
Claims and Evidence
Descriptive Essay
Expository Essay
Narrative Essay
Persuasive Essay
The Best Chat GPT Prompts For Essay Writing
Essay Sources and Presenting Research
Essay Structure
Essay Topic
Introduction
Point Evidence Explain
Referencing
Research Question
Sources of Data Collection
Transcribing Spoken Data
Australian English
British Accents
British Sign Language
Guided Discovery
Indian English
Lesson Plan
Received Pronunciation
Total Physical Response
Abbreviations
Affect or Effect
Capitalisation
Loosing or Losing
Multimodal Texts
Orthographic Features
Practice or Practise
Punctuation
Separate vs Seperate
Typographical Features
Great Vowel Shift
Inflectional Morphemes
King James Bible
Language Family
Language Isolate
Middle English
Old English Language
Scottish English
Shakespearean English
Accent vs Dialect
Bilingualism
Code Switching
Descriptivism
Descriptivism vs Prescriptivism
Dialect Levelling
English as a lingua franca
Kachru's 3 Concentric Circles
Language Changes
Pidgin and Creole
Prescriptivism
Rhotic Accent
Social Interaction
Standard English
Standardisation of English
Strevens Model of English
Technological Determinism
Vernacular English
World Englishes
Language Stereotypes
Language and Politics
Language and Power
Language and Technology
Media Linguistics
Michel Foucault Discourse Theory
Multimodality
Norman Fairclough
Behavioral Theory
Cognitive Theory
Constructivism
Critical Period
Down Syndrome Language
Functional Basis of Language
Interactionist Theory
Language Acquisition Device (LAD)
Language Acquisition Support System
Language Acquisition in Children
Michael Halliday
Multiword Stage
One-Word stage
Theories of Language Acquisition
Two-Word Stage
Williams Syndrome

Foregrounding

Grammatical Voice

Literariness

Literary Context
Literary Purpose
Literary Representation
Mode English Language
Narrative Perspective
Poetic Voice
Accommodation Theory
Bernstein Elaborated and Restricted Code
Casual Register
Concept of Face
Consultative Register
Deficit Approach
Difference Approach
Diversity Approach
Dominance Approach
Drew and Heritage Institutional Talk
Eckert Jocks and Burnouts
Formal Register
Frozen Register
Gary Ives Bradford Study
Holmes Code Switching
Intimate Register
Labov- New York Department Store Study
Language and Age
Language and Class
Language and Ethnicity
Language and Gender
Language and Identity
Language and Occupation
Marked and Unmarked Terms
Neutral Register
Peter Trudgill- Norwich Study
Phatic Talk and Banter
Register and Style
Sinclair and Coulthard
Social Network Theory
Sociolect vs Idiolect
Variety vs Standard English
Amelioration
Collocations
Colloquialisms
Compounding
Connotative Meaning
Denotative Meaning
Figurative Language
Fixed Expressions
Formal Language
Informal Language
Initialisms
Irony English Language
Levels of Formality
Lexical Ambiguity
Literary Positioning
Occupational Register
Paradigmatic Relations
Personification
Rhetorical Figures
Semantic Change
Semantic Reclamation
Syntagmatic Relations
Text Structure
Zero-Derivation
1984 Newspeak
Critical Theory
Essentialism
Forensic Linguistics
Lexicography
Linguistic Determinism
Logical Positivism
Natural Language Processing
Psycholinguistics
Rhetorical Analysis
Sapir Whorf Hypothesis
Active Listening Skills
Address Counterclaims
Group Discussion
Presentation Skills
Presentation Technology
Alternation
Compound Words
Derivational Morphemes
Lexical Morphology
Reduplication
Active Reading
Process of Elimination
Words in Context
Click Consonants
Fundamental Frequency
Interdental
International Phonetic Alphabet
Labiodental
Manner of Articulation
Monophthong
Nasal Sound
Oral Cavity
Phonetic Accommodation
Phonetic Assimilation
Place of Articulation
Sound Spectrum
Source Filter Theory
Spectrogram
Voice Articulation
Vowel Chart
Alliteration
Sound Symbolisms
Commissives
Communication Accommodation Theory
Conversational Implicature
Cooperative Principle
Declarative
Deictic centre
Deictic expressions
Expressives
Figure of Speech
Grice's Conversational Maxims
Paralanguage
Politeness Theory
Presupposition
Semantics vs. Pragmatics
Speech Acts
Aggressive vs Friendly Tone
Curious vs Encouraging Tone
Feminine Rhyme
Hypocritical vs Cooperative Tone
Masculine Rhyme
Monosyllabic Rhyme
Multisyllabic
Optimistic vs Worried Tone
Serious vs Humorous Tone
Surprised Tone
Tone English Langugage
Analyzing Informational Texts
Comparing Texts
Context Cues
Creative Writing
Digital Resources
Ethical Issues In Data Collection
Formulate Questions
Internet Search Engines
Literary Analysis
Personal Writing
Print Resources
Research Process
Research and Analysis
Technical Writing
Action Verbs
Adjectival Clause
Adverbial Clause
Anthropomorphism
Appositive Phrase
Argument from Authority
Argumentation
Auditory Description
Basic Rhetorical Modes
Begging the Question
Building Credibility
Causal Flaw
Causal Relationships
Cause and Effect Rhetorical Mode
Central Idea
Chronological Description
Circular Reasoning
Circumlocution
Classical Appeals
Classification
Close Reading
Coherence Between Sentences
Coherence within Paragraphs
Coherences within Sentences
Complex Rhetorical Modes
Compound Complex Sentences
Concessions
Concrete Adjectives
Concrete Nouns
Consistent Voice
Counter Argument
Definition by Negation
Description
Description Rhetorical mode
Direct Discourse
Equivocation
Extended Metaphor
False Connections
False Dichotomy
False Equivalence
Faulty Analogy
Faulty Causality
Fear Arousing
Gustatory Description
Hasty Generalization
Illustration
Induction Rhetoric
Levels of Coherence
Line of Reasoning
Missing the Point
Modifiers that Qualify
Modifiers that Specify
Narration Rhetorical Mode
Non-Sequitur
Non-Testable Hypothesis
Objective Description
Olfactory Description
Paragraphing
Parenthetical Element
Participial Phrase
Personal Narrative
Placement of Modifiers
Post-Hoc Argument
Process Analysis Rhetorical Mode
Red Herring
Reverse Causation
Rhetorical Fallacy
Rhetorical Modes
Rhetorical Question
Rhetorical Situation
Scare Tactics
Sentimental Appeals
Situational Irony
Slippery Slope
Spatial Description
Straw Man Argument
Subject Consistency
Subjective Description
Tactile Description
Tense Consistency
Tone and Word Choice
Transitions
Twisting the Language Around
Unstated Assumption
Verbal Irony
Visual Description
Authorial Intent
Authors Technique
Language Choice
Prompt Audience
Prompt Purpose
Rhetorical Strategies
Understanding Your Audience
Auditory Imagery
Gustatory Imagery
Olfactory Imagery
Tactile Imagery
Main Idea and Supporting Detail
Statistical Evidence
Cultural Competence
Intercultural Communication
Methodology
Research Methodology
Constituent
Object Subject Verb
Subject Verb Object
Verb Subject Object
Author Authority
Direct Quote
First Paragraph
Historical Context
Intended Audience
Primary Source
Second Paragraph
Secondary Source
Source Material
Third Paragraph
Character Analysis
Citation Analysis
Text Structure Analysis
Vocabulary Assessment

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Language analysis explores how authors/writers/speakers convey meaning through specific language analysis techniques, such as figures of speech, word choice, sentence structure, and register or tone.

Language analysis introduction

There are a number of levels at which language analysis can be done. These levels are:

Phonetics and phonology - the sound system of a language; for example, its phonemes (units of sound) and prosody (the rhythm and intonation of language).
Grammar, including morphology - the analysis of the way words, clauses, phrases and sentences are put together in English. This is sometimes called 'syntax'.
Discourse - extended communication in a range of texts and contexts.
Pragmatics - the way language is used in specific contexts.

Language analysis techniques

Tools and techniques for language analysis include: consideration of audience, literary purpose , genre , mode, and literary representation . These help readers understand language on its own terms, as well as in literary, social, and geographical contexts.

How to write a language analysis

Language analysis explores how a text:

Is shaped according to the context, audience, and the conventions of genre .
Aids the reader to explore the relationship between readers and characters.
Constructs meaning, intention, and viewpoints.

Genre groups literary texts into styles, shared conventions, settings, and themes, which is important for language analysis. Genres include poetry, novels, plays, short fiction, blogs, films etc.

For example, George RR Martin's Game of Thrones (1996 - present) series is classified as f antasy . Fantasy novels are always set in a fictional universe but are inspired by real-world myth and folklore. These novels may use magic and mythical creatures, such as dragons, as part of their genre .

Tip: 'Urban fantasy' (set in cities and urban populaces), 'wuxia' (Chinese martial arts fiction with fantasy elements), and 'fables' (stories with non-human characters) are all subgenres of fantasy.

The audience is the anticipated, or target, readership. The author must consider how the text suits the intended audience. In literary works, characters can either stand-in for the audience or directly interact with them. For example:

A chorus is a group of people who sing or comment on the dramatic actions or events in a play.
Self-conscious narrators may address their audience directly.
An 'audience surrogate' is a figure the audience can identify with because they ask questions the audience wants to be answered.

Foregrounding is a literary device which makes an image, symbol, or word a prominent or important feature in the text. It is an attention-seeking device that repeats content or breaks established patterns and calls the reader's attention to the author's language choices.

There are two foregrounding techniques to highlight at this stage:

Parallelism is a technique that repeats content with unexpected regularity. The text is foregrounded by repeated patterns and figures of speech, such as alliteration , enjambment, and anaphora.
Deviation happens when an author sets up and breaks deliberately established patterns of language and sound for effect. This is achieved through external or internal deviation.

Literariness explores the literary value of texts . Literary texts are considered to have aesthetic and moral value, akin to works of art. Such texts are written in such a way as to elevate them from other texts. They might win a literature prize like the 'Man Booker Prize', or feature in A-Level and University syllabuses.

It is worth making a distinction between Literature and literature (capital 'L' and lowercase 'l'). The literary text is the worthy, beautiful, or important text; the text which has something profound to say in the world of serious books. Literature (capital L) includes books in a canon, or collection, of important texts. Meanwhile, the term literature (lowercase 'l') covers all sorts of writing, from magazines to blogs, from genre fictions (mainstream or pulp literary works) to poems on Instagram, as well as dramas.

General ideas of literariness can be subjective. What you find meaningful or beautiful may differ from what another person finds meaningful or beautiful. However, literary theorist Roman Jakobson argues that works have a characteristic set of textual properties. Such properties include the use of certain poetic and literary devices, such as figures of speech.

Mode in the English language

Mode describes how an author communicates with their audience, and such is important for understanding language analysis. Mode is the medium of communication, the spoken or written way authors communicate ideas and themes. Mode is not the same as genre . Instead, mode is about the literary method, mood, or manner in which speech and narrative are used.

For example, authors of genre crime fiction may use a satiric or didactic mode of writing to interrogate how society glorifies violence, or purposely ignores crimes against certain races or genders. Other examples of modes include comic and pastoral.

A narrative is an account of actual or imagined events communicated directly to the reader. Narratives organize events, places, characters, and times into a coherent whole.

Language Analysis Narrative opening Once upon a time StudySmarter

Narratives are used in fiction and non-fiction, in poetry and prose. Narratologists study the theory and practice of narratives.

Narratologists analysis:

The types of narrators.
The narrative structure, which includes the literary elements which provide narrative order, plot, and setting as it is presented to the reader.
Narrative devices/techniques.
The analysis of narrative discourse (which focuses on specific language choices and structure).

Poetic voice vs. grammatical voice

The poetic voice is the speaking voice or persona that a poet or author has adopted. This voice projects a sense of identity, as well as the values and beliefs connected to that voice. There are four aspects to the poetic voice :

Grammar, including syntax.
Literary devices.
Subject matter.

The grammatical voice comprises active and passive sentences. It is the expression of the relationship between:

The predicate, the part of a sentence which contains a verb and which states something about the subject, and;
Nominal functions, the noun or object of the sentence.

Narrative perspective/point of view

Narrative perspective or point of view is the vantage point from which events of a story are filtered and then relayed to the audience . Narrative points of view are crucial for analysing language and texts because they tell the reader who is telling the story, and who sees the events .

Narrative points of view include first-person, second-person, third-person limited, and third-person omniscient.

The narrative point of view covers how the story is written and who is telling it . Narrative perspective encompasses the narrator 's voice, point of view, ideological worldview, and a focaliser (what the narrative and narrator are focused on).

To find out more about how the narrative point of view is analysed we must examine other factors that introduce bias, unreliability, or the limitations of a narrator and author's vantage point .

A text can be analysed according to its spatio-temporal viewpoint . A spatio-temporal viewpoint adopted by the narrator involves literary devices such as time frames, deixis , and flashbacks or flashforwards.
An ideological viewpoint is about the way a narrator sees the world or thinks about events (often in an extreme or polarizing way). Narrators and characters state their beliefs, values, and worldview in the narrative , which often establishes the work's themes, narrative points of view, and world-building.
The narrator is the imagined 'voice' assumed to be telling the story. How much the narrator knows of a narrated event, or how much they decide to tell the audience about it, determines the kind of narrator they are. Examples include third-person limited, omniscient, unreliable, or subjective narrators.

Tip: Consider you can analyse a narrator 's language to impart what they see, what they are doing, and where they are in a given situation.

Literary positioning

Literary positioning involves how an author establishes a relationship between themselves and the reader by declaring their stance on the subject matter . The strategies for positioning include analysing:

Personal pronouns.
Layout (which includes framing).
Lexical choices, including tone and dialect .
Synthetic personalization (the way texts communicate with us in a friendly and intimate manner despite the author having no personal relationship with the reader).

Literary purpose

Literary Purpose explains why a text was written , which allows the reader to interpret the author's aims. Texts have multiple and overlapping purposes:

To instruct (didacticism).
To be a work of art ('art for art's sake', which falls under the literary analysis rather than literature).
To entertain.
To rewrite history or 'unfactual' accounts.

Literary representation

Literary representation explores the ways meaning is constructed by linguistic techniques and language analysis , which influence the reader's perception of the subject matter of the narrative . It is the relationship between the work and what it (or what the author wants it to) represent. Representation helps readers organise their understanding of the world the text depicts, and how this relates to the real world .

Do you think novels or other works of Literature can depict real-life accurately? Or should works remain forms of escapism?

Language analysis example

We have broken down the example below to show you how to perform language analysis in manageable chunks. Along with the flashcards and revision notes we have provided, you can add your own resources as you follow along with the next part.

Let's look at Virginia Woolf's The Waves (1931). We have three tasks: to work out the intentions, the tone, and the techniques used.

'Through the chink in the hedge,' said Susan. 'I saw her kiss him. I raised my head from my flower-pot and looked through a chink in the hedge. I saw her kiss him. I saw them, Jinny and Louis, kissing. Now I will wrap my agony inside my pocket-handkerchief. It shall be screwed tight into a ball. I will go to the beech wood alone, before lessons. I will not sit at the table, doing sums. I will not sit next Jinny and next Louis. I will take my anguish and lay it upon the roots under the beech trees.

We can extrapolate various aspects of the text based on the language analysed and what we already know about the novel.

The novel explores how Woolf's characters use language to express their identity and preoccupation with their bodies and feelings . From one extract, we can tell a lot about the character which Woolf has created and understand how a little analysis can influence the reader's perception of the whole text.

How often do you find yourself watching a film and wondering who the film is for, what genre it is, and what the message of the film is? Can you apply these kinds of language analysis tools to all media?

Language Analysis - Key takeaways

Language Analysis explores how authors/writers convey meaning through specific language techniques, such as figures of speech, word choice, sentence structure, and registers or tone.
Literary representation explores the meaning of texts based on the linguistic techniques chosen by the author while analysing the relationship between the fictional world and reality.
Genre categorises literary works into recognizable styles, while mode involves the methods or manner in which language is used to communicate certain ideas and themes.
The audience is the individual or group for whom the author writes their work of literature. Literariness explores how certain audiences can assign literary value to certain texts - whether they are 'Literature', or literature.
The narrative is the account of actual or imagined events in which the narrator communicates information to the audience. This is dependent and influenced by the literary position, literary purpose , or the narrative point of view in the text.
Poetic and grammatical voices assess the presentation of the author or narrator in a text, and whether these voices imply a narrative action or the narrator 's identity.

Frequently Asked Questions about Language Analysis

--> what is a language analysis.

Language analysis explores how authors/writers/speakers convey meaning through specific language techniques, such as figures of speech, word choice, sentence structure, and registers or tone.

--> How to do language analysis?

You use the levels of language to determine how words and phrases are used in everyday speech and literature. Then, the language varieties can determine the social and cultural contexts of language used in literature and communities.

--> How to analyse an image in language analysis?

To analyse an image, you should firstly identify the medium the image is in, e.g. is it in a book? A report? A film? You should then consider different aspects such as:

Is there any written text on or around the image? If so, how does it relate to the image and what does it imply?
The visual aspects - is the image a photo? A drawing? A sign? Take into account visual design such as colour, layout, size, etc.

Consider both the denotations (literal meaning) and the connotations (cultural/associated meaning) of the image.

--> How to write a conclusion for a language analysis?

To write a conclusion for a language analysis, you should do the following:

Review the main point of your brief - why did you decide to do the analysis?
Summarise the main points of your analysis - what were your findings?
Offer a final, memorable thought - this could be a recommendation, improvement, or question.

--> How to write a language analysis introduction?

To write a language analysis introduction, you should include the following things:

A catchy hook to draw the reader in.
Background information for context.
Introduction of brief and purpose of analysis - why are you doing the analysis and what do you hope to get out of it?

Final Language Analysis Quiz

Language analysis quiz - teste dein wissen.

What are the four main families of genre?

Show answer

Comedy, romance, tragedy, satire.

Show question

Why are genres used?

To help readers’ expectations of a text, and to help authors write within (or out) of genre conventions.

What do genres do?

Genres organise information to help the reader and critics make sense of what they are about to read.

How do you analyse genres?

Genres can be analysed by the language, tone, setting, plot, and themes used.

What is the aim of genre analysis?

Genre analysis examines the style of writing to understand the writer’s intentions, target audience, theme, and reader expectations.

How do different genres form?

Genres form over time and according to what literary conventions are deemed popular by society.

Name five types of genre.

Historical fiction, romance, crime, mystery, bildungsroman, horror, science fiction, fantasy.

How many genres are there in English Literature?

There are numerous genres. However, the main four are romance, satire, comedy, and tragedy.

True or false - genre criteria is assessed only by how language is used.

Genre criteria is assessed by their overall look and imagery, how language is used, the literary techniques used, and the overall purpose of the work.

Which of the following does not apply to a crime genre?

The plot centres on a major or minor historical event.

True or false?

Aristotle posited that genres should be a fixed classification system.

Which of the following applies to a historical fiction genre?

Exploration of a type of crime, and/or focus on victims and their suffering.

A genre is the same as a theme.

What does a genre not do?

Group only novels.

Which of these is an example of a sub genre of short fiction?

Microfiction

What is the definition of literariness?

Literariness is a formal style of writing used to differentiate between texts.

When was literariness created?

Who coined the term literariness?

Roman Jakobson.

What is the aim of literariness?

It aims to differentiate between literary and non-literary texts. By doing so, it aims to find a way to quantify literature.

Who is not a literary critic of literariness?

David Miall

Measuring a text by literariness is a common practice.

Who wrote about the three components of literariness?

Literary critic David Miall wrote about the components of literariness. He said that any piece of work that was literary would possess three key components. These were a distinct style, defamiliarisation, and new experience.

How many components are there?

What are the three components of literariness?

These three components are, the presence of a distinctive writing style (almost unique to literature), defamiliarisation, and the reader engaging in a feeling in a new or unfamiliar way.

How does the first component create literariness?

The first component (the presence of a distinctive style) is able to create literariness as it involves using literary terms as a way to create defamiliarisation in the text.

What does the literary term mean?

A literary term is a term given to the different techniques that a writer can use within a text. These can include figurative language or sound and rhythm.

Literary terms are the same as literary devices.

Is a metaphor a literary term?

Yes! A metaphor is a literary term because it is a technique that can be used to cause an effect in a text. It is a type of figurative language and can help create defamiliarisation.

What is defamiliarisation?

This is a concept where a writer will take a familiar concept, and write about it in a way that is unfamiliar to the reader.

How can defamiliarisation be used to create literariness?

It can be used in order to achieve the third component (where the reader experiences something in a new or unfamiliar way). It makes the reader experience a concept, object or idea in a new way, and sot the third component is achieved.

Can defamiliarisation be created without a distinct style?

No! The first component is necessary in order to create defamiliarisation in a text.

What is the third component of literariness?

The third component of literariness is that the reader will experience a feeling in a new way. This will be achieved due to the previous two components.

How does the third component determine literariness?

This determines literariness because as outlined by Miall, this is an experience that is almost solely unique to literature.

What is literary theory?

Literary theory is the term used to describe the academic thoughts behind how literature is created.

Literariness is a type of literary theory.

What is foregrounding?

To foreground is to make an image, symbol, or language a prominent or important feature as a contrast to the background.

What is Deviation?

Deviation is an unexpected irregularity.

What is Parallelism?

Parallelism repeats content with unexpected regularity.

What are the two techniques of foregrounding?

Deviation and Parallelism.

What are the three levels of stylistic effect in foregrounding?

Grammatical level, phonetic level, semantic level.

Why do authors use foregrounding?

To draw the reader's attention to a particular word or figure in the text by using estrangement and defamiliarization.

What are the types of deviation?

Grammatical, lexical, semantic, phonological, textual, graphological, dialectal, as well as register and historical period.

What is External Deviation?

External deviation is when the author breaks from normal conventions of language use.

What is Internal Deviation?

Internal Deviation is when the author or poet breaks from a pattern they have previously set up in their work.

What is the difference between Parallelism and Repetition?

Parallelism repeats content with slight modifications or grammatical changes. Repetition is the reuse of words, phrases, or themes.

What is the synonym of 'foreground'?

The synonyms for foreground are center, focal point, and focus.

Is Foregrounding used mainly in Art or Literature?

Which of the following is not a type of deviation?

Which of the following is a technique of foregrounding?

Single Paragraph Essay

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Linguistic Analysis

2018, Meditations on the Life of Christ: The Short Italian Text. [edited by] Sarah McNamer; linguistic analysis by Pär Larson. University of Notre Dame (Indiana 46556): The Notre Dame Press.

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Michela Russo

Francesca Gambino

Il frammento del Bovo d’Antona conservato a Udine, Archivio Capitolare, Fondo Nuovi manoscritti 736.28 rappresenta un tassello della ricca tradizione europea di questa nota vicenda epica. I versi del frammento sono inoltre interessanti da un punto di vista linguistico e, soprattutto, lessicale, perché attestano numerosi lemmi e forme che non compaiono altrove. Il contributo offre al lettore uno studio linguistico del testo e rivede criticamente l’edizione che ne diede Pio Rajna nel 1887. The fragment of the Bovo d’Antona stored at Udine, Archivio Capitolare, Fondo Nuovi manoscritti represents a piece of the rich European tradition of this famous epic story. The verses of the fragment are also interesting from a linguistic point of view and, above all, lexical, because they attest many words and forms that do not appear elsewhere. The contribution gives the reader a linguistic study of the text and critically revises the edition that gave Pio Rajna in 1887. KEYWORDS Lingua e letteratura francese medievale – Lingua e letteratura franco-italiana – Code-mixing – Bovo d’Antona Medieval French Language and Literature – Medieval Franco-Italian Language and Literature – Code-mixing – Bevis of Hampton

Lorenzo Tomasin

Bollettino del Centro di studi filologici e linguistici siciliani

Marco Maggiore

The manuscript Venice, Biblioteca Nazionale Marciana, It. III, 27 (= 5008) is an important and scarcely known monument of late Medieval scientific culture in Sicily, especially in regard to disciplines like veterinary medicine and astrology. The paper focuses on an astrological text in Old Sicilian transmitted by the unique Venetian manuscript. The first critical edition of the astrological text is provided, along with a linguistic analysis and a glossary. A philological survey leads to new acquisitions about the history of the manuscript.

Roman Sosnowski

Luca D'Onghia

Asian Trading Corporation, Bangalore

Cristina Muru

Abstract The Portuguese Jesuit named Father Gaspar de Aguilar was born in 1588 in Castelo Rodrigo (Portugal) and joined the Company of Jesus in 1610. He professed four vows and studied Philosophy, Theology and Grammar. After having served the Company of Jesus by working in the Goa Mission and at the Residency of Cardiva in the Jaffnapatam Kingdom, he was dismissed from the Company of Jesus. As far as it is known, his moral behavior which was unacceptable for the Company of Jesus. Even though it seems that his behavior was not in line with a religious profile, there is no doubt about his extremely pertinent observations concerning the Tamil language. In fact, his contribution to the study and spread of Tamil with its grammar was considerable. He was a very well-read man and his Grammar book was the most exhaustive and useful source for teaching the Tamil language. The Arte itself was constantly used as a reference by other missionaries. The aim of the present article is to reconstruct the life of Father Aguilar and to present his work which has never been analysed before. I will discuss the linguistic and cultural features of Aguilar’s grammar in order to show how he was able to catch many peculiarities of the Tamil language, although these differ greatly both from his mother tongue and from Latin. Furthermore, I will discuss translations of some prayers, as well the Manual for Confession in Tamil contained at the end of the Grammar book together with some other texts such as the description of Tamil sounds and its letters. The linguistic considerations will be made taking into due consideration the historical setting and the socio-cultural context Father Aguilar experienced.

Transactions of the Philological Society 114 (1): 1-24, 2016

Andrea Sansò

The two deictic motion verbs ‘go’ and ‘come’ serve as passive auxiliaries in a handful of languages, in combination with non-finite participial forms of the main verb. Some passive constructions involving deictic motion verbs as auxiliaries are infused with special aspectual or modal meanings, even in the absence of overt aspectual or modal operators. The aim of this paper is to provide an in-depth account of the emergence of the Italian passive with andare (‘go’) + past participle, and to explore the possible crosslinguistic implications of the proposed analysis, with a view to identifying the regularities in the diachronic processes leading to the emergence of passive constructions with deictic motion verbs as auxiliaries. On the basis of a large historical corpus of Italian, we will show that the Italian motion verbs andare ‘go’ and venire ‘come’ develop into passive auxiliaries passing through a stage in which they are used as semi-copulas (with aspectual or modal values), and that this diachronic pathway appears to be valid for other languages in which such a construction type is attested.

«Diverse voci fanno dolci note». L'Opera del Vocabolario Italiano per Pietro G. Beltrami, a cura di Pär Larson, Paolo Squillacioti e Giulio Vaccaro, Alessandria, Edizioni dell'Orso, 2013,, pp. 129-136.

Chiara Gizzi

The new critical edition of Piero's treatise (vernacular version) is the starting point for a linguistic analysis of the Prospective pingendi autograph. The exam of phonetical and morphological features focuses on the vernacular characters of Borgo San Sepolcro; the most relevant syntactic structures are described. As for the lexicon, some technical words in the field of geometry, architecture and painting are presented giving an essay of the glossary of the edition.

Anna G I A C A L O N E RAMAT

Zeno Verlato

Formal and Semantic Constraints in …

Judith Meinschaefer

Shlomo Izre'el

Lorenzo Tomasin , Luca D'Onghia

Sahin Derya

Sara Ravani

Magdalena Tomaszewska

Francesco Giura

Sergey Loesov , Moscow Circle for Aramaic Studies

Christian Y Dupont

Przemysław Dębowiak , Ewa Stala

Journal of Language Contact

Agnieszka Brylak

Anna Rinaldin

Per un glossario del veneziano giustinianeo: parole lessicali di particolare interesse nelle canzonette di Leonardo Giustinian.

Marco Gregori

Diego Dotto

Helmut Satzinger

Begoña Crespo , Isabel Moskowich

Elena Artale

Antonelli, Roberto / Glessgen, Martin / Videsott, Paul (Hrsg.), Atti del XXVIII Congresso internazionale di linguistica e filologia romanza (Roma, 18-23 luglio 2016), Strasbourg, ELiPhi

Francesco Crifò

«Studi di Grammatica Italiana»

Simone Pregnolato

«Diverse voci fanno dolci note». L’Opera del Vocabolario Italiano per Pietro G. Beltrami, a cura di Pär Larson, Paolo Squillacioti e Giulio Vaccaro, Alessandria, Edizioni dell’Orso

David Stifter

Michiel de Vaan

Fabio Romanini

A Sketch of the Earliest Mongolic Language: the Brāhmī Bugut and Khüis Tolgoi Inscriptions

Alexander Vovin

Nitesh Ardekar

Alessandro Carlucci

Anthropological Linguistics

Mallory Matsumoto

Sandro Bertelli , Davide D C Cappi

John Huehnergard

Michail Kotin

«Diverse voci fanno dolci note». L'Opera del Vocabolario Italiano per Pietro G. Beltrami

Rossella Mosti

Joshua Brown

Vittorio Formentin

The Oxford Handbook of Linguistic Analysis

Bernd Heine is Emeritus Professor at the Institut für Afrikanistik, University of Cologne. He has held visiting professorships in Europe, Eastern Asia (Japan, Korea, China), Australia, Africa (Kenya, South Africa), North America (University of New Mexico, Dartmouth College), and South America (Brazil). His 33 books include Possesson: Cognitive Sources, Forces, and Grammaticalization (CUP, 1997); Auxiliaries: Cognitive Forces and Grammaticalization (OUP, 1993); Cognitive Foundations of Grammar (OUP, 1997) (with Tania Kuteva); World Lexicon of Grammaticalization (CUP, 2002); Language Contact and Grammatical Change (CUP, 2005); The Changing Languages of Europe (OUP, 2006), and The Evolution of Grammar (OUP, 2007); and with Heiko Narrog as co-editor, The Oxford Handbook of Linguistic Analysis (OUP, 2011), and The Oxford Handbook of Grammaticalization (OUP, 2012).

Heiko Narrog is professor at Tohoku University, Japan. He received a PhD in Japanese studies from the Ruhr University Bochum in 1997, and a PhD in language studies from Tokyo University in 2002. His publications include Modality in Japanese and the Layered Structure of Clause (Benjamins, 2009), Modality, Subjectivity, and Semantic Change: A Cross-Linguistic Perspective (OUP, 2012), The Oxford Handbook of Linguistic Analysis (OUP, 2010), and The Oxford Handbook of Grammaticalization (OUP, 2011), both co-edited with Bernd Heine.

A newer edition of this book is available.

This handbook compares the main analytic frameworks and methods of contemporary linguistics It offers an overview of linguistic theory, revealing the common concerns of competing approaches. By showing their current and potential applications, the book provides the means by which linguists and others can judge what are the most useful models for the task in hand. Scholars from all over the world explain the rationale and aims of over thirty explanatory approaches to the description, analysis, and understanding of language. Each chapter considers the main goals of the model; the relation it proposes between lexicon, syntax, semantics, pragmatics, and phonology; the way it defines the interaction between cognition and grammar; what it counts as evidence; and how it explains linguistic change and structure.

Front Matter

T. Givón is Distinguished Professor of Linguistics and Cognitive Science at the University of Oregon. He acquired his MA and PhD from UCLA, where he was also professor for some years. He has worked extensively with the South Ute Indian Tribe, Colorado, as

Guglielmo Cinque is Professor of Linguistics at the University of Venice, co-editor of The Oxford Handbook of Comparative Syntax (Oxford University Press, 2005) (with Richard Kayne), and author of various volumes on syntax, including Adverbs and Functional Heads (Oxford University Press, 1999) and Restructuring and Functional Heads (Oxford University Press, 2006). With Luigi Rizzi he has promoted the subseries of volumes on The Cartography of Syntactic Structures of the Oxford Studies in Comparative Syntax series edited by R. Kayne.

Luigi Rizzi is Professor of Linguistics at the University of Siena in Italy. He has been on the faculty of several universities in Europe and the US, including MIT, the University of Geneva, the Ecole Normale Supérieure (Paris). His research focuses on syntactic theory and comparative syntax, with special reference to the theory of locality, the study of variation through parametric models, the cartography of syntactic structures, and the acquisition of syntax.

Glyn Morrill has held research positions at the Centre for Cognitive Science, University of Edinburgh, Netherlands Organization for Scientific Research (NWO) Universiteit Utrecht and Centrum voor Wiskunde en Informatica (CWI) Amsterdam, and Departament de Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya. Since 1993 he has been a lecturer in Computer Science at this latter. In 1995, he co-founded with Dick Oehrle the annual Formal Grammar conference. He researches on logic in grammar and processing, focusing on type logical categorial grammar. He is author of Type Logical Grammar: Categorial Logic of Signs (Kluwer, 1994) and Ldgica de primer ordre (Edicions Universitat Politecnica de Catalunya, 2001).

Ronald W. Langacker is Professor Emeritus and Research Professor at the University of California, San Diego. Early in his career, major areas of research included generative syntax and the comparative‐historical grammar of the Uto‐Aztecan family of Native American languages. Over the last three decades, his main concern has been to develop and progressively articulate the theory of Cognitive Grammar, a radical alternative to generative theory. He has published numerous books and articles dealing with a broad array of issues in cognitive linguistics.

Jerome A. Feldman is a Professor of Computer Science and Cognitive Science at the University of California and a research scientist at the International Computer Science Institute in Berkeley. He received the Ph.D. from Carnegie-Mellon in 1964 and previously taught at Stanford and Rochester.

Laura A. Michaelis is an Associate Professor of Linguistics and a faculty fellow in the Institute of Cognitive Science at the University of Colorado at Boulder. She is the author of Aspectual Grammar and Past-Time Reference (1998) and Beyond Alternations: A Constructional Model of the German Applicative Pattern (2001) (with Josef Ruppenhofer). She is the co-editor, with Elaine Francis, of Mismatch: Form-Function Incongruity and the Architecture of Grammar (2003). She is currently collaborating on a Construction Grammar monograph with Charles Fillmore, Paul Kay, and Ivan Sag. Her work has appeared in the journals Language, Journal of Semantics, Journal of Pragmatics, Cognitive Linguistics, Journal of Linguistics, Linguistics and Philosophy , and Studies in Language .

Douglas Biber is Regents' Professor of English (Applied Linguistics) at Northern Arizona University. His research efforts have focused on corpus linguistics, English grammar, and register variation (in English and cross-linguistic; synchronic and diachronic). His publications include books on register variation and corpus linguistics published by Cambridge University Press (1988, 1995, 1998, to appear), the co-authored Longman Grammar of Spoken and Written English (1999), and more recent studies of language use in university settings and discourse structure investigated from a corpus perspective (both published by Benjamins: 2006 and 2007).

Kasia M. Jaszczolt is Reader in Linguistics and Philosophy of Language at the University of Cambridge and Fellow of Newnham College, Cambridge. She obtained her D.Phil, from the University of Oxford (1992) for a thesis on the semantics of propositional attitude reports. She has published widely on the topics of semantic ambiguity and underdetermination, definite descriptions, belief reports, and the semantics of time, principally developing and applying the theory of Default Semantics to various types of constructions. Her books include Default Semantics (Oxford University Press, 2005), Semantics and Pragmatics: Meaning in Language and Discourse (Longman, 2002), Discourse, Beliefs and Intentions (Elsevier Science, 1999) and Representing Time: An Essay on Temporality as Modality (forthcoming from Oxford University Press in 2008). She is also member of various editorial boards including Journal of Pragmatics and Studies of Pragmatics , a committee member of the Linguistics Association of Great Britain, and in from 1996 to 2008 was managing editor of a book series Current Research in the Semantics/Pragmatics Interface (CRiSPI) (Elsevier Science).

Vilmos Ágel is Chair of German Synchronic Linguistics at Kassel University. He received his doctorate (1988) and professorial degree (1997) from the ELTE Budapest. The bursaries he received include the following: 1987–8, from the German Academic Exchange Service, 1991–3 and 1998, Alexander von Humboldt-Stiftung, and 2000–4, Szécheny research bursary for professors. In 2003, he won the Friedrich Wilhelm Bessel research prize of the Alexander von Humboldt-Stiftung. Since 2004 he has been co-editor of the Zeitschrifl für Germanistische Linguistik . His main research interests are valency theory, dependency grammar, the link between contemporary grammar and the history of language, the grammar of New High German (1650–2000), and the relationship between orality/literacy and grammatical structure.

Klaus Fischer started his academic career in Britain as DAAD-Lektor (Deutscher Akademischer Austauschdienst) at the University of Wales, Lampeter (1985–88), after having studied German and Philosophy in Munich and Bonn. He has worked at London Metropolitan University or one of its predecessor institutions (City of London Polytechnic, London Guildhall University) since 1989, first as Lecturer in German, heading the German section since 1999, and from 2002 as Reader in German. In 1995, he was awarded a Ph.D. at the University of Wales. His main research interests are the foundation and application of valency theory, the comparison of English and German verb complementation, and more generally the typology of English and German.

William O’Grady is professor of linguistics at the University of Hawai‘i at Mānoa. Drawing on his expertise in first- and second-language acquisition, he has written several articles on the conditions that must be met if endangered languages are to be learned and maintained. He is currently working on Jejueo, the critically endangered language of Korea’s Jeju Island. He and two co-authors have recently published the first of a four-volume series of textbooks to support teaching of the language in high schools and colleges. A reference grammar of the language, to be published by the University of Hawai‘i Press, is forthcoming.

Mark C. Baker is Distinguished Professor of Linguistics and Cognitive Science at Rutgers University. He received his PhD in linguistics in 1985 from MIT, and previously taught at McGill University. He specializes in the syntax and morphology of less-studied languages, seeking to bring together generative-style theories and data from fieldwork and typological comparison. He has written five research monographs, including Case: Its Principles and Its Parameters (2015).

Charles J. Fillmore is Emeritus Professor at the University of California at Berkeley. His early work included contributions to generative grammar (cyclic rule application), the grammar and semantics of English verbs, case grammar, deixis, frame semantics, and construction grammar. After retirement he has been associated with FrameNet, a computational lexicography project at the International Computer Science Institute, supported mainly by the US National Science Foundation. He served as President of the Linguistic Society of America in 1991.

Collin Baker received a Ph.D. in Linguistics from University of California at Berkeley in 1999, and since 2000 has been manager of the FrameNet Project at the International Computer Science Institute. Recent publications include The Structure of the FrameNet Database (2003) (with Charles Fillmore and Beau Cronin) and FrameNet and Representing the Link between Semantic and Syntactic Relations (2004) (with Fillmore and Josef Ruppenhofer), along with a number of conference papers and tutorials at Language Resources and Evaluation Conference (LREC), Association for Computational Linguistics (ACL), and Linguistic Society of America (LSA). His current research focuses on efficiently extending the FrameNet lexicon and on annotating large corpora with semantic frames.

Martin Haspelmath is a senior staff member at the Max-Planck-Institut für evolutionäre Anthropologie in Leipzig. He received degrees at the Universität zu Köln, University at Buffalo, and the Freie Universität Berlin. He taught at the Freie Universitat (FU) Berlin, the Universitat Bamberg, the Università di Pavia, the Universität Leipzig, and at summer schools in Albuquerque, Mainz, Düsseldorf, Cagliari, Berkeley, and at MIT. His research interests are primarily in the area of broadly comparative and diachronic morphosyntax ( Indefinite Pronouns , 1997; From Space to Time , 1997; Understanding Morphology , 2002). He is one of the editors of Oxford University Press's World Atlas of Language Structures (2005).

Kees Hengeveld has been Professor of Theoretical Linguistics at the University of Amsterdam since 1996. Before that, he taught Spanish linguistics at that same university. His research focuses on Functional Discourse Grammar and linguistic typology, and often on the combination of the two. With J. Lachlan Mackenzie he published Functional Discourse Grammar (Oxford University Press, 2008). Before that, he edited Simon C. Dik's posthumous two-volume The Theory of Functional Grammar (Mouton de Gruyter, 1997) and published Non‐Verbal Predication: Theory, Typology, Diachrony (Mouton de Gruyter, 1992), as well as numerous articles and edited volumes.

J. Lachlan Mackenzie is Honorary Professor of Functional Linguistics at the Free University of Amsterdam, the Netherlands, and works at Instituto de Linguística Teórica e Computacional (Lisbon, Portugal) as a Consultant in Languages and Linguistics. His professional interests are mainly in the analysis of Western European languages from the perspective of Functional Discourse Grammar (FDG). Among recent books co-edited in this area are A New Architecture for Functional Grammar (Mouton de Gruyter, 2004), Crucial Readings in Functional Grammar (Mouton de Gruyter, 2005), and Studies in Functional Discourse Grammar (Lang, 2005). He is co-author, with Kees Hengeveld, of Functional Discourse Grammar (Oxford University Press, 2008).

Ash Asudeh is a Professor in the Department of Linguistics and the Director of the Center for Language Sciences at the University of Rochester. He has held positions at Carleton University, in the Institute of Cognitive Science, and at Oxford University, where he was Professor of Semantics in the Faculty of Linguistics, Philology, and Phonetics and a Senior Research Fellow at Jesus College. His research interests include syntax, semantics, pragmatics, language and logic, and cognitive science. He has published extensively on the syntax–semantics interface, particularly in the frameworks of Lexical Functional Grammar and Glue Semantics.

Ida Toivonen teaches linguistics and cognitive science at Carleton University in Ottawa, Canada. She received her Ph.D. from Stanford University in 2001. Her primary area of research is syntax, but she also works on phonetics, morphosyntax, lexical semantics, and language description. She is particularly interested in Swedish and Inari Saami.

Cliff Goddard's research interests lie at the intersection of language, meaning and culture. He is one of the leading proponents of the NSM (Natural Semantic Metalanguage) approach to linguistic description and a close collaborator with the originator of the approach, Anna Wierzbicka. He has published extensively on topics in semantic theory, cross-linguistic semantics, cross-cultural pragmatics, and on language description, especially in relation to English, Malay, and Yankunytjatjara (Central Australia). Recently he edited the collective volumes Ethnopragmatics: Understanding Discourse in Cultural Context (Mouton de Gruyter, 2006) and Cross-Linguistic Semantics (Benjamins, 2008). With Anna Wierzbicka, he is co-editor of the two-volume collection Meaning and Universal Grammar—Theory and Empirical Findings (Benjamins, 2002). Goddard has published two textbooks: Semantic Analysis (OUP, 1998) and The Languages of East and Southeast Asia (OUP, 2005). He is a full Professor of Linguistics at the University of New England, Australia, and a Fellow of the Australian Academy of Humanities.

Cedric Boeckx is Research Professor at the ICREA (Institució Catalana de Recerca i Estudis Avançats) and a member of the CLT (Centre de Lingüística Teòrica) at the Universitat Autònoma de Barcelona. He received his Ph.D. in linguistics in 2001 from the University of Connecticut, Storrs. He has held visiting positions at the Universities of Illinois and Maryland, and was a fellow of Netherlands Organization for Scientific Research (NWO) at Leiden University. His research interests are in theoretical syntax, comparative grammar, and architectural questions of language, including its origins and its development in children and its neurobiological basis. He is the author of, among others, Islands and Chains (Benjamins 2003), Linguistic Minimalism (Oxford University Press, 2006), Understanding Minimalist Syntax (Blackwell, 2008), and Bare Syntax (Oxford University Press, 2008). He has published numerous articles in journals such as Linguistic Inquiry and Natural Language and Linguistic Theory .

Geert Booij (1947) received an M.A. degree (cum laude) in Dutch linguistics, with minors in general linguistics and philosophy from the University of Groningen in 1971. From 1971 until 1981 he was an assistant/associate professor in the Department of Dutch of the University of Amsterdam, where he obtained his Ph.D. degree in 1977 with the dissertation Dutch Morphology. A Study of Word Formation in Generative Grammar (Foris Publications, Dordrecht). He was a professor of general linguistics at the Vrije Universiteit Amsterdam (1981-2005), and at Leiden University (2005-2012). He is now an emeritus professor. He is the founder and editor of the book series Yearbook of Morphology and its successor, the journal Morphology , author of The Phonology of Dutch (1995), The Morphology of Dutch (2002), Construction Morphology (2010), and of The Grammar of Words (2005, 2012), all published by Oxford University Press, and of linguistic articles in a wide range of linguistic journals, mainly on phonology and morphology.

Maria Gouskova is an Assistant Professor in the Department of Linguistics at New York University. She works in phonology and its interfaces with phonetics and morphology in the framework of Optimality Theory. She is interested in the relation of phonological constraints to each other and to linguistic primitives. She has published in the journals Linguistic Inquiry and Phonology on a variety of topics in phonological theory ranging from the role of sonority in syllable structure to templates in reduplication.

Henriëtte de Swart is Professor of French Linguistics and Semantics at Utrecht University (the Netherlands). She obtained her Ph.D. from the University of Groningen with a thesis entitled Adverbs of quantification: A generalized quantifier approach (1991). She works on topics in tense and aspect, negation, and indefinites. Her publications on tense and aspect include Meaning and use of not … until (Journal of Semantics, 1996), Aspect shift and coercion (NLLT, 1998), Aspectual implication of plural indefinites (2006), A cross-linguistic discourse analysis of the perfect (Journal of Pragmatics, 2007). She also wrote An introduction to natural language semantics (CSLI, 1998).

Joost Zwarts teaches at the Department of Linguistics at Utrecht University. He previously worked as a researcher in the PIONIER project ‘Case Cross-linguistically’ at Radboud University Nijmegen. His main interests are in (formal and lexical) semantics and syntax, especially in the behaviour of prepositions and articles. He has done fieldwork in Kenya and published The Phonology of Endo (Lincom, 2004).

Ray Jackendoff is Seth Merrin Professor Emeritus of Philosophy and former Co-director of the Center for Cognitive Studies at Tufts University, and Research Affiliate in the Department of Brain and Cognitive Sciences at MIT. He has written extensively on syntax, semantics, morphology, music cognition, social cognition, consciousness, and the architecture of the language faculty and its place in the mind. He is a recipient of the Jean Nicod Prize in Cognitive Philosophy and of the Rumelhart Prize in Theoretical Foundations of Human Cognition, and he has served as President of both the Linguistic Society of America and the Society for Philosophy and Psychology.

Yan Huang is Professor of Linguistics at the University of Auckland. He has previously taught linguistics at the University of Cambridge, University of Oxford, and University of Reading, where he was Professor of Theoretical Linguistics. His main research interests are in pragmatics, semantics and syntax, especially the pragmatics–semantics interface and the pragmatics–syntax interface. His published work includes The Syntax and Pragmatics of Anaphora (Cambridge University Press, 1994, re-issued 2007), Anaphora: A Cross-Linguistic Study (Oxford University Press, 2000), and Pragmatics (Oxford University Press, 2007). He has also published a number of articles and reviews in leading international journals of linguistics.

Rens Bod obtained his Ph.D. in Computational linguistics at the University of Amsterdam. He currently holds a chair in Artificial Intelligence and is affiliated with the University of St Andrews and at the University of Amsterdam. His main interests are in computational models of language acquisition and statistical natural language processing, but he has also worked in computational musicology, reasoning, and in the history of science and humanities. Rens published over 90 scholarly articles and is the author of Beyond Grammar: An Experience-Based Theory of Language (Stanford University, 1998). He also co-edited a number of handbooks, including Probabilistic Linguistics (MIT Press, 2003) and Data-Oriented Parsing (CSLI Publications/Chicago, 2003).

Eric Pederson (PhD 1991) is associate professor of Linguistics at the University of Oregon. The overarching theme of his research is the relationship between language and conceptual processes. He was a student at the University of California, Berkeley, working within Cognitive Linguistics with George Lakoff, Dan Slobin, Eve Sweetser, and Leonard Talmy since 1980. He joined the Max Planck Institute for Psycholinguistics in 1991 until 1997, where he began working on issues more specific to linguistic relativity. Relevant publications include “Geographic and Manipulable Space in Two Tamil Linguistic Systems” (1993); “Language as Context, Language as Means: Spatial Cognition and Habitual Language use” (1995); “Semantic Typology and Spatial Conceptualization” (with Eve Danziger, Stephen Levinson, Sotaro Kita, Gunter Senft, and David Wilkins, 1998); “Through the Looking Glass: Literacy, Writing Systems and Mirror Image Discrimination” (with Eve Danziger, 1998); and “Mirror-Image Discrimination among Nonliterate, Monoliterate, and Biliterate Tamil Speakers” (2003). In addition to linguistic relativity, his general interests include semantic typology, field/descriptive linguistics (South India), and the representation of events. Eric Pederson can be reached at [email protected].

Francisco Yus teaches pragmatics at the University of Alicante, Spain. He has a Ph.D. in linguistics and has specialised in the application of pragmatics (especially relevance theory) to media discourses and conversational issues. He has applied pragmatics to alternative comics ( Conversational Co-operation in Alternative Comics , 1995; El discurso femenino en el cómic alternativo inglés , 1998), proposed a verbal-visual model of communication in media discourses ( La interpretación y la imagen de masas , 1997), and developed a pragmatic approach to Internet-mediated communication ( Ciberpragmática , 2001). Latest research has to do with the application of relevance theory to misunderstandings and irony in conversation, as well as to the production and interpretation of humor.

Robert D. Van Valin, Jr. received his Ph.D. in linguistics at the University of California, Berkeley. His research is focused on theoretical linguistics, especially syntactic theory and theories of the acquisition of syntax and the role of syntactic theory in models of sentence processing. He is the co-author of Functional Syntax and Universal Grammar (Cambridge University Press, 1984), the editor of Advances in Role and Reference Grammar (Benjamins, 1993), the co-author of Syntax: Structure, Meaning and Function (Cambridge University Press, 1997), and the author of An Introduction to Syntax (Cambridge University Press, 2001). His most recent book is Exploring the Syntax-Semantics Interface (Cambridge University Press, 2005). He is the general editor of the Oxford Surveys in Syntax and Morphology series (Oxford University Press).

Sherman Wilcox (PhD 1988) is Professor of Linguistics at the University of New Mexico. His main research interests are the theoretical and applied studies of signed languages. His theoretical work focuses on iconicity, gesture, and typological studies of signed languages. ↵He is widely recognized as an advocate for academic acceptance of American Sign Language in universities in the United States. He also has taught signed language interpreting for many years and most recently has begun to demonstrate the application of Cognitive Linguistics to interpreting theory. He is author of several books and articles, including The Phonetics of Fingerspelling (1992); Gesture and the Nature of Language (with David F. Armstrong and William C. Stokoe, 1994); Learning to See: Teaching American Sign Language as a Second Language (with Phyllis Perrin Wilcox, 1997); and several edited collections.

Phyllis Perrin Wilcox is Professor in the Department of Linguistics at the University of New Mexico. She has presented on metaphor and metonymy in signed languages at national and international conferences. Dr. Wilcox was instrumental in establishing the baccalaureate degree in Signed Language Interpreting at UNM and coordinated the program for over fifteen years. She has served as chair of the Registry of Interpreters for the Deaf's National Review Board and now serves on the New Mexico Interpreting Licensure Board. Dr. Wilcox is the author of Metaphor in American Sign Language (Gallaudet University Press, 2000) and numerous articles on metaphorical cognition.

Peter W. Culicover is Distinguished University Professor at the Ohio State University. He was awarded the Humboldt Research Award in 2006. His primary research has been in syntactic theory. He has been concerned with exploring the cognitive and computational factors that underlie the foundations of syntactic theory. Most recently he has been pursuing an evolutionary account of the origin of grammars from a constructional perspective.

Alice Caffarel is Senior Lecturer in the School of Languages and Cultures at the University of Sydney, where she teaches French, French linguistics and stylistics. She is the author of a Systemic Functional Grammar of French and the co-editor of a volume on language typology from a systemic functional perspective.

Joan L. Bybee (Ph.D. linguistics, University of California at Los Angeles, 1973) was on the faculty at the State University of New York at Buffalo from 1973 until 1989 and is now Distinguished Professor Emerita of the Department of Linguistics at the University of New Mexico. Bybee's research interests include theoretical issues in phonology and morphology, language universals and linguistic change. Her books include Morphology (1985), The Evolution of Grammar (1994) (with Revere Perkins and William Pagliuca), Phonology and Language Use (2001) and Frequency of Use and the Organization of Language (2006). In 2004 she served as the President of the Linguistic Society of America.

Clay Beckner is a Ph.D. candidate in Linguistics at the University of New Mexico. In his research, he studies usage-based language change with a variety of methodologies, including corpus-based statistical methods, psycholinguistics, and computational modeling. He is working on a dissertation that addresses the formation of multi-word units in language, incorporating experimental data and exemplar models of usage and change.

Richard Hudson (PhD 1961) is emeritus professor of linguistics at University College London. His main research interests involve the overlapping fields of lexical semantics, syntax, morphology, speech processing, and sociolinguistics. His publications include the textbook Sociolinguistics (1981, 1996) and the following monographs: English Complex Sentences: An Introduction to Systemic Grammar (1971); Arguments for a Non-transformational Grammar (1976); Word Grammar (1984); and English Word Grammar (1990). He also has a strong interest in applying linguistics in education. He was elected a Fellow of the British Academy in 1993. His involvement with Cognitive Linguistics dates from the 1970s, when he first heard about prototypes in a lecture by George Lakoff at a time when he was also learning about knowledge representation in Artificial Intelligence and was developing a rather cognitive view of sociolinguistics; since then, his research and ideas have converged increasingly on those of the leading cognitive linguists, and they now fit comfortably in this tradition. He has been a consulting editor of the journal Cognitive Linguistics since its foundation.

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How to write a literary analysis essay | A step-by-step guide

How to Write a Literary Analysis Essay | A Step-by-Step Guide

Published on January 30, 2020 by Jack Caulfield . Revised on September 2, 2022.

Literary analysis means closely studying a text, interpreting its meanings, and exploring why the author made certain choices. It can be applied to novels, short stories, plays, poems, or any other form of literary writing.

A literary analysis essay is not a rhetorical analysis , nor is it just a summary of the plot or a book review. Instead, it is a type of argumentative essay where you need to analyze elements such as the language, perspective, and structure of the text, and explain how the author uses literary devices to create effects and convey ideas.

Before beginning a literary analysis essay, it’s essential to carefully read the text and c ome up with a thesis statement to keep your essay focused. As you write, follow the standard structure of an academic essay :

An introduction that tells the reader what your essay will focus on.
A main body, divided into paragraphs , that builds an argument using evidence from the text.
A conclusion that clearly states the main point that you have shown with your analysis.

Step 1: reading the text and identifying literary devices, step 2: coming up with a thesis, step 3: writing a title and introduction, step 4: writing the body of the essay, step 5: writing a conclusion.

The first step is to carefully read the text(s) and take initial notes. As you read, pay attention to the things that are most intriguing, surprising, or even confusing in the writing—these are things you can dig into in your analysis.

Your goal in literary analysis is not simply to explain the events described in the text, but to analyze the writing itself and discuss how the text works on a deeper level. Primarily, you’re looking out for literary devices —textual elements that writers use to convey meaning and create effects. If you’re comparing and contrasting multiple texts, you can also look for connections between different texts.

To get started with your analysis, there are several key areas that you can focus on. As you analyze each aspect of the text, try to think about how they all relate to each other. You can use highlights or notes to keep track of important passages and quotes.

Language choices

Consider what style of language the author uses. Are the sentences short and simple or more complex and poetic?

What word choices stand out as interesting or unusual? Are words used figuratively to mean something other than their literal definition? Figurative language includes things like metaphor (e.g. “her eyes were oceans”) and simile (e.g. “her eyes were like oceans”).

Also keep an eye out for imagery in the text—recurring images that create a certain atmosphere or symbolize something important. Remember that language is used in literary texts to say more than it means on the surface.

Narrative voice

Ask yourself:

Who is telling the story?
How are they telling it?

Is it a first-person narrator (“I”) who is personally involved in the story, or a third-person narrator who tells us about the characters from a distance?

Consider the narrator’s perspective . Is the narrator omniscient (where they know everything about all the characters and events), or do they only have partial knowledge? Are they an unreliable narrator who we are not supposed to take at face value? Authors often hint that their narrator might be giving us a distorted or dishonest version of events.

The tone of the text is also worth considering. Is the story intended to be comic, tragic, or something else? Are usually serious topics treated as funny, or vice versa ? Is the story realistic or fantastical (or somewhere in between)?

Consider how the text is structured, and how the structure relates to the story being told.

Novels are often divided into chapters and parts.
Poems are divided into lines, stanzas, and sometime cantos.
Plays are divided into scenes and acts.

Think about why the author chose to divide the different parts of the text in the way they did.

There are also less formal structural elements to take into account. Does the story unfold in chronological order, or does it jump back and forth in time? Does it begin in medias res —in the middle of the action? Does the plot advance towards a clearly defined climax?

With poetry, consider how the rhyme and meter shape your understanding of the text and your impression of the tone. Try reading the poem aloud to get a sense of this.

In a play, you might consider how relationships between characters are built up through different scenes, and how the setting relates to the action. Watch out for dramatic irony , where the audience knows some detail that the characters don’t, creating a double meaning in their words, thoughts, or actions.

Your thesis in a literary analysis essay is the point you want to make about the text. It’s the core argument that gives your essay direction and prevents it from just being a collection of random observations about a text.

If you’re given a prompt for your essay, your thesis must answer or relate to the prompt. For example:

Essay question example

Is Franz Kafka’s “Before the Law” a religious parable?

Your thesis statement should be an answer to this question—not a simple yes or no, but a statement of why this is or isn’t the case:

Thesis statement example

Franz Kafka’s “Before the Law” is not a religious parable, but a story about bureaucratic alienation.

Sometimes you’ll be given freedom to choose your own topic; in this case, you’ll have to come up with an original thesis. Consider what stood out to you in the text; ask yourself questions about the elements that interested you, and consider how you might answer them.

Your thesis should be something arguable—that is, something that you think is true about the text, but which is not a simple matter of fact. It must be complex enough to develop through evidence and arguments across the course of your essay.

Say you’re analyzing the novel Frankenstein . You could start by asking yourself:

Your initial answer might be a surface-level description:

The character Frankenstein is portrayed negatively in Mary Shelley’s Frankenstein .

However, this statement is too simple to be an interesting thesis. After reading the text and analyzing its narrative voice and structure, you can develop the answer into a more nuanced and arguable thesis statement:

Mary Shelley uses shifting narrative perspectives to portray Frankenstein in an increasingly negative light as the novel goes on. While he initially appears to be a naive but sympathetic idealist, after the creature’s narrative Frankenstein begins to resemble—even in his own telling—the thoughtlessly cruel figure the creature represents him as.

Remember that you can revise your thesis statement throughout the writing process , so it doesn’t need to be perfectly formulated at this stage. The aim is to keep you focused as you analyze the text.

Finding textual evidence

To support your thesis statement, your essay will build an argument using textual evidence —specific parts of the text that demonstrate your point. This evidence is quoted and analyzed throughout your essay to explain your argument to the reader.

It can be useful to comb through the text in search of relevant quotations before you start writing. You might not end up using everything you find, and you may have to return to the text for more evidence as you write, but collecting textual evidence from the beginning will help you to structure your arguments and assess whether they’re convincing.

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To start your literary analysis paper, you’ll need two things: a good title, and an introduction.

Your title should clearly indicate what your analysis will focus on. It usually contains the name of the author and text(s) you’re analyzing. Keep it as concise and engaging as possible.

A common approach to the title is to use a relevant quote from the text, followed by a colon and then the rest of your title.

If you struggle to come up with a good title at first, don’t worry—this will be easier once you’ve begun writing the essay and have a better sense of your arguments.

“Fearful symmetry” : The violence of creation in William Blake’s “The Tyger”

The introduction

The essay introduction provides a quick overview of where your argument is going. It should include your thesis statement and a summary of the essay’s structure.

A typical structure for an introduction is to begin with a general statement about the text and author, using this to lead into your thesis statement. You might refer to a commonly held idea about the text and show how your thesis will contradict it, or zoom in on a particular device you intend to focus on.

Then you can end with a brief indication of what’s coming up in the main body of the essay. This is called signposting. It will be more elaborate in longer essays, but in a short five-paragraph essay structure, it shouldn’t be more than one sentence.

Mary Shelley’s Frankenstein is often read as a crude cautionary tale about the dangers of scientific advancement unrestrained by ethical considerations. In this reading, protagonist Victor Frankenstein is a stable representation of the callous ambition of modern science throughout the novel. This essay, however, argues that far from providing a stable image of the character, Shelley uses shifting narrative perspectives to portray Frankenstein in an increasingly negative light as the novel goes on. While he initially appears to be a naive but sympathetic idealist, after the creature’s narrative Frankenstein begins to resemble—even in his own telling—the thoughtlessly cruel figure the creature represents him as. This essay begins by exploring the positive portrayal of Frankenstein in the first volume, then moves on to the creature’s perception of him, and finally discusses the third volume’s narrative shift toward viewing Frankenstein as the creature views him.

Some students prefer to write the introduction later in the process, and it’s not a bad idea. After all, you’ll have a clearer idea of the overall shape of your arguments once you’ve begun writing them!

If you do write the introduction first, you should still return to it later to make sure it lines up with what you ended up writing, and edit as necessary.

The body of your essay is everything between the introduction and conclusion. It contains your arguments and the textual evidence that supports them.

Paragraph structure

A typical structure for a high school literary analysis essay consists of five paragraphs : the three paragraphs of the body, plus the introduction and conclusion.

Each paragraph in the main body should focus on one topic. In the five-paragraph model, try to divide your argument into three main areas of analysis, all linked to your thesis. Don’t try to include everything you can think of to say about the text—only analysis that drives your argument.

In longer essays, the same principle applies on a broader scale. For example, you might have two or three sections in your main body, each with multiple paragraphs. Within these sections, you still want to begin new paragraphs at logical moments—a turn in the argument or the introduction of a new idea.

Robert’s first encounter with Gil-Martin suggests something of his sinister power. Robert feels “a sort of invisible power that drew me towards him.” He identifies the moment of their meeting as “the beginning of a series of adventures which has puzzled myself, and will puzzle the world when I am no more in it” (p. 89). Gil-Martin’s “invisible power” seems to be at work even at this distance from the moment described; before continuing the story, Robert feels compelled to anticipate at length what readers will make of his narrative after his approaching death. With this interjection, Hogg emphasizes the fatal influence Gil-Martin exercises from his first appearance.

Topic sentences

To keep your points focused, it’s important to use a topic sentence at the beginning of each paragraph.

A good topic sentence allows a reader to see at a glance what the paragraph is about. It can introduce a new line of argument and connect or contrast it with the previous paragraph. Transition words like “however” or “moreover” are useful for creating smooth transitions:

… The story’s focus, therefore, is not upon the divine revelation that may be waiting beyond the door, but upon the mundane process of aging undergone by the man as he waits.

Nevertheless, the “radiance” that appears to stream from the door is typically treated as religious symbolism.

This topic sentence signals that the paragraph will address the question of religious symbolism, while the linking word “nevertheless” points out a contrast with the previous paragraph’s conclusion.

Using textual evidence

A key part of literary analysis is backing up your arguments with relevant evidence from the text. This involves introducing quotes from the text and explaining their significance to your point.

It’s important to contextualize quotes and explain why you’re using them; they should be properly introduced and analyzed, not treated as self-explanatory:

It isn’t always necessary to use a quote. Quoting is useful when you’re discussing the author’s language, but sometimes you’ll have to refer to plot points or structural elements that can’t be captured in a short quote.

In these cases, it’s more appropriate to paraphrase or summarize parts of the text—that is, to describe the relevant part in your own words:

The conclusion of your analysis shouldn’t introduce any new quotations or arguments. Instead, it’s about wrapping up the essay. Here, you summarize your key points and try to emphasize their significance to the reader.

A good way to approach this is to briefly summarize your key arguments, and then stress the conclusion they’ve led you to, highlighting the new perspective your thesis provides on the text as a whole:

By tracing the depiction of Frankenstein through the novel’s three volumes, I have demonstrated how the narrative structure shifts our perception of the character. While the Frankenstein of the first volume is depicted as having innocent intentions, the second and third volumes—first in the creature’s accusatory voice, and then in his own voice—increasingly undermine him, causing him to appear alternately ridiculous and vindictive. Far from the one-dimensional villain he is often taken to be, the character of Frankenstein is compelling because of the dynamic narrative frame in which he is placed. In this frame, Frankenstein’s narrative self-presentation responds to the images of him we see from others’ perspectives. This conclusion sheds new light on the novel, foregrounding Shelley’s unique layering of narrative perspectives and its importance for the depiction of character.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Caulfield, J. (2022, September 02). How to Write a Literary Analysis Essay | A Step-by-Step Guide. Scribbr. Retrieved June 9, 2023, from https://www.scribbr.com/academic-essay/literary-analysis/

Is this article helpful?

Jack Caulfield

Other students also liked, how to write a thesis statement | 4 steps & examples, academic paragraph structure | step-by-step guide & examples, how to write a narrative essay | example & tips, lewis dolan.

Hi, I'm wondering if you could address the structure of a literary analysis for a 10,000 word dissertation? Thanks

Shona McCombes (Scribbr Team)

Like an essay, a literary analysis dissertation should include an introduction and a conclusion. The body text should be divided into chapters with clear headings (2 or 3 body chapters would generally be appropriate for a dissertation of this length). Apart from that, there are no set rules for the structure – it just needs to be logically organized.

If you're analyzing several longer texts, you could focus on one text per chapter; if you're working with more than one author, you could focus on a different author per chapter; if you're working on a wider variety of texts (or on a single text), you can organize the chapters by theme or topic.

It might help to think of your dissertation as a series of smaller interrelated essays: each chapter should have a logical internal structure of its own, with an introduction and conclusion paragraph. In the main introduction and conclusion, you should make it clear how all the chapters fit together and contribute to your overall argument.

I hope that's helpful!

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Academic Writing

How to Write a Language Analysis

Last Updated: April 30, 2020 References Approved

This article was co-authored by Jamie Korsmo, PhD . Jamie Korsmo is a Ph.D. candidate in English at Georgia State University. There are 11 references cited in this article, which can be found at the bottom of the page. wikiHow marks an article as reader-approved once it receives enough positive feedback. In this case, 88% of readers who voted found the article helpful, earning it our reader-approved status. This article has been viewed 166,843 times.

Understanding how to structure and write a language analysis is a useful skill that is necessary to succeed in many academic settings and college courses. Strong language analysis essays identify how the author of a particular piece of writing uses words to sway her readers' opinions. This type of essay provides the reader with a detailed analysis of the rhetorical devices used by an author and elucidates how these techniques persuade readers.

Understanding the Format

Image titled Write a Language Analysis Step 1

For a language analysis, the text to be analyzed is usually chosen for you by your teacher. So you won't have to panic about choosing an appropriate text to analyze.

Image titled Write a Language Analysis Step 2

The effectiveness of this type of essay depends on your ability to parse through the source material and uncover the moments of persuasion present, identify these moments, and explain their effectiveness to the reader. To this end, you must familiarize yourself with different kinds of rhetorical devices and persuasive techniques used by writers.

Image titled Write a Language Analysis Step 3

Some common persuasive techniques include logical fallacies and rhetorical appeals (to ethos, pathos, or logos).

Getting Started

Image titled Write a Language Analysis Step 4

Try an initial scan, followed by a more thorough detail reading. Reading once broadly and another time in detail can help you to define the overall ideas of the article or articles. Then go back and focus on the details you want to use in your language analysis.

Image titled Write a Language Analysis Step 5

Taking notes as you go (by highlighting or underlining) will save you a lot of time later when you want to go back to the text for details to support your claims.

Image titled Write a Language Analysis Step 6

Understanding an author's intention and point of view will help you organize your own thoughts and formulate your analysis.

Identifying Rhetorical Language Use

Image titled Write a Language Analysis Step 7

Rhetorical situations usually involve employing language that is intended to persuade someone toward a particular view or belief. That is why rhetoric is important in a language analysis essay. These types of essays aim to uncover specific language used by authors in order to persuade readers.

Image titled Write a Language Analysis Step 8

For example, if an author uses the word terminate to indicate an ending, this has a much more final, definite ending than to simply say that something is finished or came to an end. Choosing this word over others is a deliberate act on the part of the author, one that can be interrogated in a language analysis essay.

Image titled Write a Language Analysis Step 9

An appeal to ethos is an ethical appeal that emphasizes the reliability or credibility of the author and their sources to prove a point.
An appeal to logos is a logical application of evidence that appeals to the readers sense of logic or reason.
An appeal to pathos is a rhetorical technique that weighs on people's emotions to sway their opinion one way or another.

Image titled Write a Language Analysis Step 10

One example of a logical fallacy is a hasty generalization. [4] X Research source This fallacy involves reaching a conclusion before you have gathered adequate evidence on the subject. An example of a hasty generalization would be that all cows are black with white spots because you've seen three cows and all three of them were black with white spots.
Another example of a logical fallacy is a slippery slope argument. [5] X Research source This fallacy involves arguing that if one event is allowed to happen, it will inevitably lead to an extreme and undesirable result. An example of a slippery slope argument would be: If we allow one hotel to be built on the lake, pretty soon the whole place will be worse than Las Vegas.

Image titled Write a Language Analysis Step 11

In his play As You Like It, William Shakespeare’s Jacques famously says, All the world's a stage, / And all the men and women merely players: / They have their exits and their entrances. [7] X Research source This is a metaphor that compares the action of real life with the action of a theatrical play. Shakespeare says that world is a stage and all the people are actors, not merely that they are like actors.

Image titled Write a Language Analysis Step 12

For example, saying she was as quiet as a mouse is an analogy that lets the reader know something about the subject, she , by relating a fact about her to a fact everyone knows (that mice are quiet).

Writing Your Analysis

Image titled Write a Language Analysis Step 13

A good thesis statement is concise and clear. It tells the reader what the point of the paper is and why it's important. The thesis must make a claim of some sort. [8] X Research source
For this type of essay, your thesis claim will probably be something like this: Through his use of ______ (whatever rhetorical technique you think your author employs), this author attempts to _______ (whatever you think the purpose of his persuasion is).
Here is an example of a strong thesis statement: Excessive meat consumption in America is the leading cause of pollution today, and, thus, is a significant influence on global warming. This thesis makes a claim (specifically a cause and effect claim) about a debatable topic with a narrow enough focus to create an interesting, manageable argumentative essay.
Here is an example of a weak thesis statement: Pollution is a problem in the world today. This is not a debatable issue; few people would argue that pollution is not a problem. The topic is also too broad. You can't write a paper on every single aspect of pollution.

Image titled Write a Language Analysis Step 14

An example of a three-part thesis statement might look something like this: Global warming is caused by industrial pollution, automobile exhaust fumes, and waste dumping in the oceans. In this case, you would expect to find three body paragraphs: one about industrial pollution, one about car exhaust fumes, and one about trash in the ocean. Any other causes of pollution would not fit anywhere in this essay, which restricts the meaning and the message of the paper.
Changing the thesis to avoid this form will make for a much more functional essay that is written at a more advanced level. A more effective thesis would be something like this: Due to increasing global temperatures and rising ocean levels, global warming has become an issue that needs to be acknowledged by a wider audience in order to begin reversing the effects.

Image titled Write a Language Analysis Step 15

A good introduction should give enough background information that the reader feels intrigued and knows what to look for in the rest of the paper.

Image titled Write a Language Analysis Step 16

Be sure to review your main points and restate your thesis. But make sure not to introduce any new information in the conclusion so that you can effectively wrap up what you've already said.

Revising and Applying Final Touches

Image titled Write a Language Analysis Step 18

Sentence fragments. [10] X Research source Fragments are incomplete phrases that cannot stand alone as a sentence because they are missing either a verb, a noun, or a complete thought.
Parallelism. [11] X Research source Errors in parallelism occur when words or groups of words do not appear in the same format or structure within a sentence.
Subject-verb agreement. [12] X Research source Errors with subject-verb agreement happen when an incorrect verb form is used with a particular subject. For example, he know instead of he knows.

Image titled Write a Language Analysis Step 22

Expert Q&A

Compare or contrast multiple articles. In an assignment that involves multiple pieces of source material, it can be extremely helpful to compare and contrast these clearly as part of the overall language analysis. ⧼thumbs_response⧽ Helpful 0 Not Helpful 0
Always read the syllabus or special instructions. Many teachers who assign this type of work will include specific directions for those who need to complete it. Make sure you pay attention to these in creating the best and most effective language analysis. ⧼thumbs_response⧽ Helpful 0 Not Helpful 0

↑ https://owl.english.purdue.edu/owl/resource/625/01/
↑ https://owl.english.purdue.edu/owl/resource/588/04/
↑ http://www.logicallyfallacious.com/index.php/logical-fallacies/101-hasty-generalization
↑ http://www.logicallyfallacious.com/index.php/logical-fallacies/163-slippery-slope
↑ http://literarydevices.net/metaphor/
↑ http://shakespeare.mit.edu/asyoulikeit/asyoulikeit.2.7.html
↑ https://owl.english.purdue.edu/owl/resource/588/01/
↑ https://owl.english.purdue.edu/owl/section/1/5/
↑ https://owl.english.purdue.edu/owl/resource/620/01/
↑ https://owl.english.purdue.edu/owl/resource/623/01/
↑ https://owl.english.purdue.edu/owl/resource/599/01/

About This Article

A language analysis is an essay that explores how an author tries to sway their readers about a subject. While you’re reading your text, highlight examples where the author's trying to convince you of something. Some common methods of persuasion include using metaphors, writing with emotional language, and using logical fallacies. Then, try to figure out what the author's overall goal is and come up with a thesis for your paper. For example, your thesis might look something like, "Through his use of emotional language, the author is attempting to persuade his readers that excessive meat consumption causes pollution." Write a paragraph for each method of persuasion and include quotes from the text to support your thesis. For more tips from our English co-author, including how to conclude your language analysis, read on! Did this summary help you? Yes No

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Computer Science > Computation and Language

Title: red teaming language model detectors with language models.

Abstract: The prevalence and high capacity of large language models (LLMs) present significant safety and ethical risks when malicious users exploit them for automated content generation. To prevent the potentially deceptive usage of LLMs, recent works have proposed several algorithms to detect machine-generated text. In this paper, we systematically test the reliability of the existing detectors, by designing two types of attack strategies to fool the detectors: 1) replacing words with their synonyms based on the context; 2) altering the writing style of generated text. These strategies are implemented by instructing LLMs to generate synonymous word substitutions or writing directives that modify the style without human involvement, and the LLMs leveraged in the attack can also be protected by detectors. Our research reveals that our attacks effectively compromise the performance of all tested detectors, thereby underscoring the urgent need for the development of more robust machine-generated text detection systems.

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AQA English Language Paper 1 June 2023

Subject: English

Age range: 14-16

Resource type: Lesson (complete)

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8 June 2023

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Linguistic Analysis: Memory and Language

DateDespite the decades of meticulous research, the notion of linguistic studies still has a variety of aspects that require further examination. One of these aspects concerns the emergence of a language as a verbal system of communication. Among the series of theories, researchers find it hard to identify the most appropriate one. Some of the theories include a focus on the human intention to communicate, the speech itself, or syntax, while others claim the language evolution to be multicomponent (Fitch, 2017). Such a variety of approaches to language makes it more to complicated to define the way of the language classification.

At some point, linguistic studies required morphological typology basis upon which further examination would be possible. Thus, at the beginning of the 19th century, German poet and translator A. W. von Schlegel defined the approach to language classification by dividing them into the groups of synthetic and analytic languages (Haspelmath & Michaelis, 2017). Whereas synthetic languages perform grammatical variations with the help of inflections, analytic ones tend to make use of word combinations and auxiliary verbs. Due to such distinction, languages vary significantly in their case marking, verb agreement, and syntax. In the course of this assignment, the data of one of the indigenous languages of Peru will be examined with the help of contrastive and descriptive analyses.

The first part of the research will be dedicated to the analysis of the language’s phonetic and morphemic structure. A word, being a universally accepted language unit bearing independent semantic meaning, consists of morphemes, which, in their turn, fall into phonemes. According to scholars, morphemes are divided into free and bound, where free morphemes can exist as a separate word, and bound morphemes bear only grammatical meaning (Velupillai, 2012). In order to define the further construction of the given language, it is necessary to specify whether the words include bound morphemes and case inflections.

The variation of the verbal representation of the word woman will be used to demonstrate the construction. Among the four examples, one can define four variations of the word, including warmita, warmi, warmikuna, warmikunata . By analyzing this word, it may be noticed that the morpheme warmi- is present in all words, forming the word’s root. Velupillai (2012) defines the root as “the smallest unit with any semantic content” (p. 90).

Other morphemes present in words include bound morphemes, or inflections -ta-, and -kuna-. The range of inflections allows one to assume that the indigenous language of Peru given in the assignment is synthetic. Researchers claim that synthetic languages do not have to pay as much attention to syntax as analytic ones (AlA’amiri & Jameel, 2019). As the English language is a vivid example of an analytic language, its syntactic structure will be compared later in the paper.

In the further course of the investigation, the case marking of the language will be defined. Case marking is primarily designed to identify the constituents’ agreement within a syntactical unit (Avetisyan, Lago, & Vasishth, 2020). In order to define the grammatical aspect of case marking of the language provided, the following sentences are to be compared:

warmita rikun ‘he/she sees the woman’;
warmi rikun ‘the woman sees him/her.’

The provided syntactical unit shows the subject-object juxtaposition by shifting the word woman . As it was mentioned previously, analytic languages pay much attention to syntax in order to convey the semantic context behind the structure. Such a process of grammatical relations change is evident in the example of English sentences, where woman has shifted from a sentence subject to an object of an action (Tallerman, 2014). Thus, the basic constituent order, expressed by subject (S), verb (V), and object (O) within a sentence is obligatory in order to define the agent, or the doer of the action (Whaley, 1996).

However, once the sentences of the given language are analyzed, it may be noticed that the order of constituents remained the same in both cases. By means of an Adaptive Grammar model of the segmentation algorithm, which focuses on identifying the morphological units, the inflectional morphemes can be estimated (Loukatou, Stoll, Blasi, & Cristia, 2018). The primary difference between derivational and inflectional morphemes lies in the fact that latter ones are not able to change the part of speech, modifying the word’s grammatical category of number or person instead.

The grammatical relation is instead marked by case inflection -ta, forming the word’s Genitive case. The presence of this case inflection signifies the existence of other cases common to synthetic languages like Latin or Spanish. Another important aspect of nouns is the ways they modify in terms of number. The category of quantity is one of the universals for practically any language family (Mingazova, Subich, & Shangaraeva, 2016).

In the case of the given language, the plural form of the noun also depends on the constituent’s position in the syntactical unit. Hence, warmikuna ‘woman’ as a subject is formed with the help of suffix – kuna -. However, once the constituent’s role shifts to the action’s object, the Genitive case of the noun is added, forming the word warmikunata .

The role of the noun within a sentence is also of crucial importance for the language. In English, for example, the agent and the action are of equal significance for the syntactical unit, making the verb take the position right after the noun. Researchers claim that on the level of subconsciousness, recipients tend to perceive the information better once the order of constituents is fixed (Arantzeta et al., 2017). In the given language, the order is similar to Latin, where the nouns are placed before the verb like in the sentence warmi runtuta dalirqa ‘the woman hit an egg.’

Having analyzed the noun by means of contrastive analysis, it was established that the given language falls into the category of synthetic languages. The evidence for this statement is in the presence of a wide variety of inflectional affixes. Additionally, the word order in a sentence is similar to that of Latin, which, while having free word order, still aims at preserving the sequence of the subject, object, and verb (Devine & Stephens, 2017). In order to define other major language characteristics, it is necessary to dwell upon the verb descriptive analysis.

Verb, as a part of speech, has always been a significant constituent of both language and its syntactical structure. The verb usually helps the recipient estimate some additional information about the action and its agent. For comparison, an English verb as part of a sentence defines the object of the unit, which usually follows the action (Kilby, 2019). However, with different constituents order in the given language, the verb should include more features in order to define the sentence’s subject and object.

In order to take a closer look at this part of the speech, the word speak will be analyzed. To begin with, it is necessary to note the categories verb usually has in a language. According to de Haan, major categories expressed by this part of speech are the ones of tense, aspect, and modality (Song, 2011). Additionally, verbs also tend to present grammatical categories of person, number, and semantic categories of situations and events (Dickey, 2016). Hence, speaking of the word speak , its first-person singular form in the present is rimani , while the same past form is rimarqani. The third-person singular forms are riman and rimarga , respectively. Finally, the third person plural forms in the present and past are rimanku and rimarqaku .

Based on this data, it may be concluded that while all the forms have the root rima -, the inflections help define the verb’s categories of absolute tense, person, and number. The division of absolute tense implies the moment when the action happened (past, present, or future) in relation to the moment of speaking (Merkibayev, Seisenbayeva, Bekkozhanova, Koblanova, & Alikhankyzy, 2018). Unfortunately, the data provided is not exhaustive to examine the grammatical categories of aspect and modality.

In order to finish the analysis, the issue of alignment in the language should be mentioned. In linguistics, alignment marks the relations between the arguments of the transitive and intransitive verbs (Zúñiga, 2018). Having synthetic characteristics, the given language presents the Nominative-Accusative type of alignment, which is primarily characterized by placing the subject of the sentence in a Nominative case, while the object obtains Accusative or Genitive case.

Considering the investigation, it may be concluded that the language presented for the study is characterized by major features of a synthetic language. Some of the most remarkable characteristics include a wide variety of case marking and inflectional affixes, SOV constituent order in the sentence with Nominative-Accusative type of morphosyntactic alignment. However, in order to define more features of the language, a wider range of empirical data should be provided.

AlA’amiri, B. F. K., & Jameel, A. F. (2019). Morphological Typology: A Comparative Study of Some Selected Languages. Journal of College of Education/Wasit , 1 (37), 709-724.

Arantzeta, M., Bastiaanse, R., Burchert, F., Wieling, M., Martinez-Zabaleta, M., & Laka, I. (2017). Eye-tracking the effect of word order in sentence comprehension in aphasia: evidence from Basque, a free word order ergative language. Language, Cognition and Neuroscience , 32 (10), 1320-1343.

Avetisyan, S., Lago, S., & Vasishth, S. (2020). Does case marking affect agreement attraction in comprehension? Journal of Memory and Language , 112 , 104087.

Devine, A. M., & Stephens, L. D. (2017). Towards a Syntax-Semantics Interface for Latin. Catalan journal of linguistics , 16 , 79-100.

Dickey, S. (2016). Lexical and grammatical aspect. The Routledge Handbook of Semantics , 338-353.

Fitch, W. T. (2017). Empirical approaches to the study of language evolution. Psychonomic bulletin & review , 24 (1), 3-33.

Haspelmath, M., & Michaelis, S. M. (2017). Analytic and synthetic. In Language Variation-European Perspectives VI: Selected papers from the Eighth International Conference on Language Variation in Europe (ICLaVE 8), Leipzig, May 2015 (Vol. 19, p. 3). John Benjamins Publishing Company.

Kilby, D. (2019). Descriptive syntax and the English verb . London, United Kingdom: Routledge.

Loukatou, G., Stoll, S., Blasi, D., & Cristia, A. (2018). Modeling infant segmentation of two morphologically diverse languages. In Actes de la conférence Traitement Automatique de la Langue Naturelle, TALN (Vol. 1, pp. 47-57).

Merkibayev, T., Seisenbayeva, Z., Bekkozhanova, G., Koblanova, A., & Alikhankyzy, G. (2018). Oppositions in the conceptual and linguistic category of time. Opción , 34 (85-2), 116-148.

Mingazova, N. G., Subich, V. G., & Shangaraeva, L. (2016). The Semantic Morphological Category of Noun Number in Structurally Different Languages. International Journal of Environmental and Science Education , 11 (15), 8387-8402.

Song, J. J. (Ed.). (2011). The Oxford handbook of linguistic typology . Oxford, United Kingdom: Oxford University Press.

Tallerman, M. (2014). Understanding syntax . London, United Kingdom: Routledge.

Velupillai, V. (2012). An introduction to linguistic typology . Amsterdam, Netherlands: John Benjamins.

Whaley, L. J. (1996). Introduction to typology: the unity and diversity of language . New York, NY: Sage.

Zúñiga, F. (2018). The diachrony of morphosyntactic alignment. Language and linguistics compass , 12 (9), e12300.

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