Fluctuations of text complexity: the case of Basic State Examination in English

The demand for a Russian dictionary with indices of abstractness/concreteness of words has been expressed in a number of areas including linguistics, psychology, neurophysiology and cognitive studies focused on imaging concepts in human cognitive systems. Although dictionaries of abstractness/concreteness were compiled for a number of languages, Russian has been recently viewed as an under-resourced language for the lack of one. The Laboratory of Quantitative Linguistics of Kazan Federal University has implemented two methods of compiling dictionaries of abstract/concrete words, i.e. respondents survey and extrapolation of human estimates with the help of an original computer program. In this article, we provide a detailed description of the methodology used for assessing abstractness/concreteness of words by native Russian respondents, as well as control algorithms validating the survey quality. The implementation of the methodology has enabled us to create a Russian dictionary (1500 words) with indices of concreteness/abstractness of words, including those missing in the Russian Semantic Dictionary by N.Yu. Shvedova (1998). We have also created three versions of a machine dictionary of abstractness/concreteness based on the extrapolation of the respondents' ratings. The third, most accurate version contains 22,000 words and has been compiled with the use of a modern deep learning technology of neural networks. The paper provides statistical characteristics (histograms of the distribution of ratings, dispersion, etc.) of both the machine dictionary and the dictionary obtained by interviewing informants. The quality of the machine dictionary was validated on a test set of words by means of contrasting machine and human evaluations with the latter viewed as more credible. The purpose of the paper is to give a detailed description of the methodology employed to create a concrete/abstract dictionary, as well as to demonstrate the methodology of its application in theoretical and applied research on concrete examples. The paper shows the practical use of this vocabulary in six case studies: predicting the complexity of school textbooks as a function of the share of abstract words; comparing abstractness indices of Russian-English equivalents; assessing concreteness/abstractness of polysemantic words; contrasting ratings of different age groups of respondents; contrasting ratings of respondents with different levels of education; analyzing concepts of "concreteness” and “specificity”.

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Section: приложения словаря: сложность текстовunclassified

Russian dictionary with concreteness/abstractness indices

Solovyev

Volskaya

Andreeva

et al. 2022

Russian Journal of Linguistics

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“…There is blind trust in readability formulas to set forth a complexity label to text. In practice, the popular Flesch Kinkaid Grade Level (FKGL) is commonly used by language teachers to grade language-level readings or compare English textbooks Zhang (2016), Khodadady and Mehrazmay (2017), Cárcamo Morales (2020) and Gizatulina et al (2020). Nevertheless, it is known that there are differences between text complexity for native speakers and secondlanguage learners (Natova, 2019).…”

Section: Related Workmentioning

confidence: 99%

A Model Text Recommendation System for Engaging English Language Learners: Facilitating Selections on CEFR

Escobar-Acevedo¹,

Guerrero-García²,

Guzmán-Cabrera³

2022

rupkatha

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A pedagogically informed multimodal education system is defined by how well reading tasks are assigned to students in a contemporary classroom. A source that becomes a provider of readings is the web, where it is possible to find information on practically all areas of knowledge and in a wide variety of languages. However, selecting the appropriate material for the level and theme becomes a tedious job to which language teachers must devote a significant amount of their time. Selecting suitable readings to accompany the teaching-learning process is thus not a ‘trivial’ task. Basic-level texts for language competence are easy to recognize and obtain but as is seen in the case of the Common European Framework of Reference for Languages recommendations (CEFR), selection of appropriate texts that impart language competencies, especially of vocabulary and grammar at higher levels of communicativeness, selection becomes increasingly complex for teachers. Furthermore, the suggested readings should be raked by complexity in accordance with student capabilities. We suggest, that automatic classifiers based on CEFR levels may help in this process of selections from the already available corpora of authentic texts on the web. The existing facility of access of readers to such material on the web may come to the aid of automated classifiers. Teachers use interest to motivate reading in classrooms, but automatic recommendation systems will allow specific or even individualized recommendations. The authors explore the impact of such multimodal methods on the acquisition of better linguistic and communicative skills.

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“…English, which is favored as the predominant language of communication, has six main lexical categories distinguished by their morphological properties and semantic features: noun, verb, adjective, adverb, preposition, conjunction, determiner, and exclamation (Gizatulina et al, 2020;Suwastini et al, 2023). From the extent of the study that discusses language and linguistics, this study is intended to analyze verbs in English, seeing their use as the main feature in a sentence (Krismayani et al, 2021;Lin et al, 2017;Yusefi et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Natural Semantic Metalanguage Analysis of English Speech Act Verb ‘Inform’

Ardiantari

2023

IJLL

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Generally, every language has a lexicon that is grouped into classified verbs. Each verb can be broken down into some specific subtypes. Particularly in English, verbs are semantically divided into several subtypes. Verbs such as tell, ask, inform, offer, forgive, and others are belonging to the types of verbs of speech or speech acts. This study aims to analyze natural semantic metalanguage analysis of English speech act verb of inform. In collecting data is using observational method. Therefore, the theory of natural semantic metalanguage (NSM) is used to analyze the data taken from the American Corpus (COCA). By way of explication, the particular lexicon is explained thoroughly to reveal the subtle meaning of semantic features without any bias. The research argues that the four subtypes of the speech act verb "inform" demonstrate varying semantic forms and structures. The distinctions in lexicon become evident within their common semantic domain. Moreover, the four associated lexicons with the verb "inform" possess unique semantic traits and serve specific purposes within speech acts.

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Fluctuations of text complexity: the case of Basic State Examination in English

Cited by 8 publications

References 5 publications

Russian dictionary with concreteness/abstractness indices

Russian dictionary with concreteness/abstractness indices

A Model Text Recommendation System for Engaging English Language Learners: Facilitating Selections on CEFR

Natural Semantic Metalanguage Analysis of English Speech Act Verb ‘Inform’

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