The dramatic expansion of modern linguistic research and enhanced accuracy of linguistic analysis have become a reality due to the ability of artificial neural networks not only to learn and adapt, but also carry out automate linguistic analysis, select, modify and compare texts of various types and genres. The purpose of this article and the journal issue as a whole is to present modern areas of research in computational linguistics and linguistic complexology, as well as to define a solid rationale for the new interdisciplinary field, i.e. discourse complexology. The review of trends in computational linguistics focuses on the following aspects of research: applied problems and methods, computational linguistic resources, contribution of theoretical linguistics to computational linguistics, and the use of deep learning neural networks. The special issue also addresses the problem of objective and relative text complexity and its assessment. We focus on the two main approaches to linguistic complexity assessment: “parametric approach” and machine learning. The findings of the studies published in this special issue indicate a major contribution of computational linguistics to discourse complexology, including new algorithms developed to solve discourse complexology problems. The issue outlines the research areas of linguistic complexology and provides a framework to guide its further development including a design of a complexity matrix for texts of various types and genres, refining the list of complexity predictors, validating new complexity criteria, and expanding databases for natural language.

This paper introduces an adaptation of the open source ReaderBench framework that now supports Russian multilevel analyses of text characteristics, while integrating both textual complexity indices and state-of-the-art language models, namely Bidirectional Encoder Representations from Transformers (BERT). The evaluation of the proposed processing pipeline was conducted on a dataset containing Russian texts from two language levels for foreign learners (A - Basic user and B - Independent user). Our experiments showed that the ReaderBench complexity indices are statistically significant in differentiating between the two classes of language level, both from: a) a statistical perspective, where a Kruskal-Wallis analysis was performed and features such as the “nmod” dependency tag or the number of nouns at the sentence level proved the be the most predictive; and b) a neural network perspective, where our model combining textual complexity indices and contextualized embeddings obtained an accuracy of 92.36% in a leave one text out cross-validation, outperforming the BERT baseline. ReaderBench can be employed by designers and developers of educational materials to evaluate and rank materials based on their difficulty, as well as by a larger audience for assessing text complexity in different domains, including law, science, or politics.

In this paper, we apply usage-based linguistic analysis to systematize the inventory of orthographic errors observed in the writing of non-native users of Russian. The data comes from a longitudinal corpus (560K tokens) of non-native academic writing. Traditional spellcheckers mark errors and suggest corrections, but do not attempt to model why errors are made. Our approach makes it possible to recognize not only the errors themselves, but also the conceptual causes of these errors, which lie in misunderstandings of Russian phonotactics and morphophonology and the way they are represented by orthographic conventions. With this linguistically-based system in place, we can propose targeted grammar explanations that improve users’ command of Russian morphophonology rather than merely correcting errors. Based on errors attested in the non-native academic writing corpus, we introduce a taxonomy of errors, organized by pedagogical domains. Then, on the basis of this taxonomy, we create a set of mal-rules to expand an existing finite-state analyzer of Russian. The resulting morphological analyzer tags wordforms that fit our taxonomy with specific error tags. For each error tag, we also develop an accompanying grammar explanation to help users understand why and how to correct the diagnosed errors. Using our augmented analyzer, we build a webapp to allow users to type or paste a text and receive detailed feedback and correction on common Russian morphophonological and orthographic errors.

Text complexity assessment is a challenging task requiring various linguistic aspects to be taken into consideration. The complexity level of the text should correspond to the reader’s competence. A too complicated text could be incomprehensible, whereas a too simple one could be boring. For many years, simple features were used to assess readability, e.g. average length of words and sentences or vocabulary variety. Thanks to the development of natural language processing methods, the set of text parameters used for evaluating readability has expanded significantly. In recent years, many articles have been published the authors of which investigated the contribution of various lexical, morphological, and syntactic features to the readability level. Nevertheless, as the methods and corpora are quite diverse, it may be hard to draw general conclusions as to the effectiveness of linguistic information for evaluating text complexity due to the diversity of methods and corpora. Moreover, a cross-lingual impact of different features on various datasets has not been investigated. The purpose of this study is to conduct a large-scale comparison of features of different nature. We experimentally assessed seven commonly used feature types (readability, traditional features, morphological features, punctuation, syntax frequency, and topic modeling) on six corpora for text complexity assessment in English and Russian employing four common machine learning models: logistic regression, random forest, convolutional neural network and feedforward neural network. One of the corpora, the corpus of fiction literature read by Russian school students, was constructed for the experiment using a large-scale survey to ensure the objectivity of the labeling. We showed which feature types can significantly improve the performance and analyzed their impact according to the dataset characteristics, language, and data source.

This article explores the word-formation dimension of learner text complexity which indicates how skilful the non-native speakers are in using more and less complex - and varied - derivational constructions. In order to analyse the association between complexity and writing accuracy in word formation as well as interactive effects of task type, text register, and native language background, we examine the materials of the REALEC corpus of English essays written by university students with Russian L1. We present an approach to measure derivational complexity based on the classification of suffixes offered in Bauer and Nation (1993) and then compare the complexity results and the number of word formation errors annotated in the texts. Starting with the hypothesis that with increasing complexity the number of errors will decrease, we apply statistical analysis to examine the association between complexity and accuracy. We found, first, that the use of more advanced word-formation suffixes affects the number of errors in texts. Second, different levels of suffixes in the hierarchy affect derivation accuracy in different ways. In particular, the use of irregular derivational models is positively associated with the number of errors. Third, the type of examination task and expected format and register of writing should be taken into consideration. The hypothesis holds true for regular but infrequent advanced suffixal models used in more formal descriptive essays associated with an academic register. However, for less formal texts with lower academic register requirements, the hypothesis needs to be amended.

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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