Computational Linguistics Research Articles

Emerging applications of artificial intelligence in pathogen genomics

The analysis of microbial genomes has long been recognised as a complex and data-rich domain where artificial intelligence (AI) can assist. As AI technologies have matured and expanded, pathogen genomics has also contended with exponentially larger datasets and an expanding role in clinical and public health practice. In this mini-review, we discuss examples of emerging applications of AI to address challenges in pathogen genomics for precision medicine and public health. These include models for genotyping whole genome sequences, identifying novel pathogens in metagenomic next generation sequencing, modelling genomic information using approaches from computational linguistics, phylodynamic estimation, and using large language models to make bioinformatics more accessible to non-experts. We also examine factors affecting the adoption of AI into routine laboratory and public health practice and the need for a renewed vision for the potential of AI to assist pathogen genomics practice.

In search of an optimal method for analyzing deep structures: frame semantics and classification of argumentative structures

The subject of the research is the search for the optimal method of analyzing deep structures using frame semantics. The study of semantic roles, similarities and differences in the approaches of both C. Fillmore and B. Levin – M. R. Hovav make it possible to analyze the structure of a sentence in more detail and accurately, identify deep cases and determine semantic relations between words. The study of these aspects is key to understanding language constructs and their interpretation. The study of various approaches makes it possible to identify both common features and unique features, which is key for a complete understanding of language constructions. An interest in text analysis in the field of artificial intelligence, machine learning and computational linguistics, and an understanding of the semantic relationships between words will help create more accurate and efficient text processing algorithms. One of the research methods is the semantic analysis of sentences based on corpus data. This method includes the study of various linguistic constructions in the context of their use in real texts, which allows us to identify common patterns and rules for the use of these linguistic units in different situations. The scientific novelty of the study lies in the fact that the authors have determined the similarity of the approaches on how to understand the surface and deep structures of language of Ch. Fillmore and B. Levin and M. Rappaport. Their work, despite differences in methodology and terminology, together allow for in-depth investigation of the relationship between the meanings of verbs and the structure of arguments. As a result of the study, the natural relationships between deep cases and semantic roles in sentences of various types are revealed, and key points that need to be taken into account when analyzing deep structures for a more accurate definition of the semantic roles of arguments are highlighted: frame semantics and thematic grids. Disagreements and alternative points of view contribute to the constant development and improvement of linguistic theories. Such debates eventually lead to a deeper understanding of the implementation of the arguments and open up opportunities for further research in this area. Both C. Fillmore and B. Levin and M. Rappaport have made significant contributions to understanding the surface and deep structures of language, although their approaches and terminology may differ.

Evaluations of Large Language Models a Bibliometric analysis

The development of artificial intelligence (AI) and the increased curiosity about how large language models (LLMs) may maximize an organization's opportunities and the ethical implications of LLMs, such as the ability to generate human-like text, give rise to concerns regarding disinformation and fake news. As a result, it is crucial to develop evaluation benchmarks that take into account the social and ethical implications involved. The great challenges of LLMs lack awareness of their own limitations, yet they persist in producing responses to the best of their capabilities. This often results in seemingly plausible but ultimately incorrect answers, posing challenges to the implementation of reliable generative AI in industry. This paper aims to delve into the evaluation metrics of machine-learning models' performance, specifically focusing on LLM. Therefore, bibliometric analysis utilized to explore and analyze various techniques and methods used in evaluating large language models. Additionally, it sheds light on the specific areas of focus when evaluating these models. The results show that natural language processing systems, classification of information, and computational linguistics are some of the techniques used to evaluate large language models. This work paves the way for future investigations employing extensive language models.

Этнокультурная история и современное измерение Ордена знак почета института истории, языка и литературы УФИЦ РАН: от башкирского академцентра к федеральному институту

The Institute of History, Language and Literature of the Ufa Federal Research Center of the Russian Academy of Sciences is a federal academic institution with a humanitarian profile, the key research area of which is comprehensive Bashkir studies. The Institute, which celebrated its centenary in 2022, has gone a long way from the Society for the Study of Bashkiria at the Academic Center of the People’s Commissariat of Education of Bashkiria, which united ascetic researchers, to the authoritative scientific center of the Russian Academy of Sciences. The institution has made and continues to make an invaluable contribution to the study of the archaeology, ancient and modern history, and material and spiritual culture of the multinational region. The Bashkir people, one of the many ethnic groups in the Russian Federation, are the focus of this work. The team’s contributions, which have been recognized in the scientific community of Russia and beyond, not only have a scientific value but also have applied and practical significance. These contributions demonstrate the researchers’ ability to solve problems that face modern humanitarian science, in a qualitative manner. His century-old scientific biography and ethnocultural history include significant archaeological discoveries, fundamental monographic and collective research, scientific collections, multi-volume collections of the Bashkir folk art, anthologies of the Bashkir literature, numerous linguistic and encyclopedic dictionaries that incorporated creative ideas, thoughts, achievements of several generations of scientists (M.A. Burangulov, T.H. Akhmadiev, N.V. Bikbulatov, I.M. Gvozdikova, R.G. Kuzeev, H.F. Usmanov, M.V. Murzabulatov, S.N. Shitova, N.A. Mazhitov, A.H. Pshenichnyuk, N.H. Maksyutova, S.F. Mirzhanova, Z.G. Uraksin, F.G. Khisamitdinova, A.I. Kharisov, G.B. Khusainov, N.T. Zaripov, A.M. Suleymanov, F.A. Nadrshina, etc.). From the study of archaeology in the Southern Urals region and its ancient history and culture, to understanding modern socio-cultural processes and modern history in Bashkiria, anthropology, ethnography, and the Bashkir language research, computational linguistics, dialectology, folk art, literary studies, archeography, and the study of written and printed materials – these are the main research areas of the Institute’s scientists.

Polysemy—Evidence from Linguistics, Behavioral Science, and Contextualized Language Models

Abstract Polysemy is the type of lexical ambiguity where a word has multiple distinct but related interpretations. In the past decade, it has been the subject of a great many studies across multiple disciplines including linguistics, psychology, neuroscience, and computational linguistics, which have made it increasingly clear that the complexity of polysemy precludes simple, universal answers, especially concerning the representation and processing of polysemous words. But fuelled by the growing availability of large, crowdsourced datasets providing substantial empirical evidence; improved behavioral methodology; and the development of contextualized language models capable of encoding the fine-grained meaning of a word within a given context, the literature on polysemy recently has developed more complex theoretical analyses. In this survey we discuss these recent contributions to the investigation of polysemy against the backdrop of a long legacy of research across multiple decades and disciplines. Our aim is to bring together different perspectives to achieve a more complete picture of the heterogeneity and complexity of the phenomenon of polysemy. Specifically, we highlight evidence supporting a range of hybrid models of the mental processing of polysemes. These hybrid models combine elements from different previous theoretical approaches to explain patterns and idiosyncrasies in the processing of polysemous that the best known models so far have failed to account for. Our literature review finds that (i) traditional analyses of polysemy can be limited in their generalizability by loose definitions and selective materials; (ii) linguistic tests provide useful evidence on individual cases, but fail to capture the full range of factors involved in the processing of polysemous sense extensions; and (iii) recent behavioral (psycho) linguistics studies, large-scale annotation efforts, and investigations leveraging contextualized language models provide accumulating evidence suggesting that polysemous sense similarity covers a wide spectrum between identity of sense and homonymy-like unrelatedness of meaning. We hope that the interdisciplinary account of polysemy provided in this survey inspires further fundamental research on the nature of polysemy and better equips applied research to deal with the complexity surrounding the phenomenon, for example, by enabling the development of benchmarks and testing paradigms for large language models informed by a greater portion of the rich evidence on the phenomenon currently available.

Functional characterization of the language network of polyglots and hyperpolyglots with precision fMRI.

How do polyglots-individuals who speak five or more languages-process their languages, and what can this population tell us about the language system? Using fMRI, we identified the language network in each of 34 polyglots (including 16 hyperpolyglots with knowledge of 10+ languages) and examined its response to the native language, non-native languages of varying proficiency, and unfamiliar languages. All language conditions engaged all areas of the language network relative to a control condition. Languages that participants rated as higher proficiency elicited stronger responses, except for the native language, which elicited a similar or lower response than a non-native language of similar proficiency. Furthermore, unfamiliar languages that were typologically related to the participants' high-to-moderate-proficiency languages elicited a stronger response than unfamiliar unrelated languages. The results suggest that the language network's response magnitude scales with the degree of engagement of linguistic computations (e.g. related to lexical access and syntactic-structure building). We also replicated a prior finding of weaker responses to native language in polyglots than non-polyglot bilinguals. These results contribute to our understanding of how multiple languages coexist within a single brain and provide new evidence that the language network responds more strongly to stimuli that more fully engage linguistic computations.

БАШКИРСКОЕ ЯЗЫКОЗНАНИЕ В ДЕЯТЕЛЬНОСТИ ОРДЕНА ЗНАК ПОЧЕТА ИНСТИТУТА ИСТОРИИ, ЯЗЫКА И ЛИТЕРАТУРЫ УФИЦ РАН

This article examines the main stages in the development of Bashkir linguistics and the research directions of the Bashkir language at the Order of the Badge of Honor Institute of History, Language, and Literature of the Ufa Federal Research Center of the Russian Academy of Sciences. From the very beginning, the linguistic department has been actively pursuing its goals. Over the course of a century, the department has become one of the most prominent centers specializing in the analysis of spoken and written Bashkir, as well as the study of dialects and varieties. Linguistic research projects in connection with the culture, literature, and history of the Bashkir people are carried out with the most active participation from the Institute’s linguists. They have developed and established the norms of the modern literary language, which have been revised several times over the course of a century. Issues related to improving and regulating literary norms of the language continue to be central to the work of the Department of Linguistics staff. Due to the established strong traditions for fixing linguistic units, significant success has been achieved in the fields of Bashkir lexicography and terminography. On the basis of these efforts, generalizing works on dialectology, lexicology, and phraseology of the Bashkir language have been produced. It was important to study the history of the Bashkir language. These studies provide a fairly complete picture of the stages in the formation of linguistic science and the major stages of language development in the Republic of Bashkortostan. An exemplary analysis of the grammatical structure of modern Bashkir reveals its main phonetic, morphologic, morphematic, and syntactic characteristics. The work of the Department of Linguistics staff has contributed significantly to the development of Bashkir onomastic, ethnolinquistic, and sociolinguistic research. The latest research directions based on the applied developments of computational linguistics enrich the Bashkir linguistics with new aspects and approaches, bring the Bashkir language to the world level. The issues of development, the current state and prospects for improving the Bashkir language in diachrony and synchrony receive comprehensive study and detailed coverage in the activities of the staff of the Linguistics Department of the Institute.

Computational linguistics and its role in the Kurdish language Orthography and voice as a model

Today information technology has permeated all aspects of life One of the aspect which is directly affected and under hegemony of technology is language, since language is the most important means of communication, and the whole human society communicate and understand each other through language. With any changes and develops (progress) of life society language changes and progresses parallels, this why technology reflected in language Rather, a special branch of language has emerged is computer language that works on the coordination between computer and language progress The content of this research discuss( focus on) the beginnings of this kind of linguistics( language) and its role in Orthography and phopnology in kurdish language

MATHEMATICAL MODEL OF FUZZY DEFINITION OF SUBJECTS OF SCIENTIFIC ARTICLES USING SYNTACTICALLY RELATED WORDS

Mathematical methods are widely used to formalize the solution of problems of automatic text processing, including text classification. To classify texts, a “naive” Bayes classifier, methods of -nearest neighbors, decision trees, support vector machines, distribution of letter combinations (character -grams), logistic regression, and approaches based on artificial neural networks are used. These methods are used by modern computational linguistics. Classical computational linguistics considers text as a carrier of meaning. The purpose of the study is to formalize the definition of the topics of scientific articles using syntactically related words by a mathematical model, and therefore classical computer linguistics. We propose an abstract mathematical model of fuzzy classification based on common objects. The model assumes that some objects belong to only one class and are class identifiers. The result of a fuzzy classification can be one or more classes. For each class a degree of membership is determined. We propose a specification of the model with in which the common objects are syntactically related pairs of words. We conclude syntactically related words are more promising in research than verbal bigrams determined by sentence order. We propose the model in our international study. The study involve the creation of a corpus of scientific articles and their distribution by topic. The corpus will be used to classify scientific articles by topic.

An Accounting Classification System Using Constituency Analysis and Semantic Web Technologies

ABSTRACT To aid accountants in making professional judgments and decisions regarding the accounting methods for transactions, we propose a classification system by integrating computational linguistics with semantic web technologies. We use constituency parsing to convert the classification rules in accounting standards into a machine-processable data structure: Resource Description Framework (RDF) triples. When an accounting classification question is input, the system converts it into an RDF triple, compares it with the established triples of different accounting methods, and subsequently identifies the most appropriate accounting method. We showcased and evaluated our proposed model using IFRS 9 and IAS 28. Our study provides both scholarly and practical applications by (1) incorporating computational linguistics and semantic web technologies to create an interpretable, process-traceable, and explainable classification system aligned with regulatory requirements; and (2) proving that the knowledge-based model can be established without substantial training data, enhancing its accessibility and utility for accounting professionals. Data Availability: Data are available from the authors upon request.

Language modernization in applied, experimental, and computer linguistics aspects during the translator training in Kazakhstan

Relevance. The research relevance is predefined by the need to modernize the educational system of Kazakhstan in terms of training translators using numerous technological online resources for the most effective implementation of the language modernization strategy and improvement of the text quality attributes in a globalized world. Purpose. The research aims to study the fundamental techniques of applied, experimental, and computer linguistics, which are the basis of the professional activity of the translator, the prospects of national languages with the help of online resources (CAT programs, data corpuses, terminological online dictionaries), evaluation of technological perspectives at the present stage of social development. Methodology. The methodology of this research is based on a theoretical understanding of the linguistic and interdisciplinary studies of authors, the comparative study of methods and techniques in the context of applied, experimental, computer linguistics, introduced in the Kazakh educational process in translator training. Results. This research analyzes the main technologies used for language modernization, considering the industry specifics of each considered linguistic branch, the analysis of machine text recognition mechanisms, algorithms used to improve oral speech and written texts. Moreover, the principles and prospects of language modernization were analyzed, the optimized methodology of studying the programs based on the artificial intelligence in the training of translators in Kazakhstan was developed. The materials presented in this research can be used to further improve the professional skills of translators, to develop pedagogical models related to language modernization. Conclusions. This study underscores the imperative of integrating modern technological resources into translator training in Kazakhstan to effectively implement language modernization strategies and enhance text quality attributes in a globalized context. The analysis of applied, experimental, computer linguistics techniques, coupled with the evaluation of online resources, highlights the potential for optimizing translator education, advancing the national languages amidst contemporary technological advancements. Keywords: automated systems; online resources; CAT programs; linguistic methods; educational process

Grouping sounds into evolving units for the purpose of historical language comparison.

Computer-assisted approaches to historical language comparison have made great progress during the past two decades. Scholars can now routinely use computational tools to annotate cognate sets, align words, and search for regularly recurring sound correspondences. However, computational approaches still suffer from a very rigid sequence model of the form part of the linguistic sign, in which words and morphemes are segmented into fixed sound units which cannot be modified. In order to bring the representation of sound sequences in computational historical linguistics closer to the research practice of scholars who apply the traditional comparative method, we introduce improved sound sequence representations in which individual sound segments can be grouped into evolving sound units in order to capture language-specific sound laws more efficiently. We illustrate the usefulness of this enhanced representation of sound sequences in concrete examples and complement it by providing a small software library that allows scholars to convert their data from forms segmented into sound units to forms segmented into evolving sound units and vice versa.

Enhanced Multi-Class Newsgroup Document Classification Using N-Gram Approach

With the advancement of technology, consumption of data and information over web is increasing day by day. This has led to rising dependence of our lives on internet data at personal and professional fronts. Being a populous nation with high internet penetration, India is a huge market in digital media. The adaptability to online news, has created a large number of text news data which requires classification into categories for further applications. To address the work for efficient news document classification in the field of computational linguistics, we have proposed a novel approach for Newsgroup classification by combining tokens with a context window size of one, two and three known as unigram, bigram and trigram respectively. 
 In our research work, we explored the relevance of trigram by experimenting over different collection of n-grams using three supervised classifiers namely Perceptron, Nearest Centroid, and Random Forest. In the experiment, we found that inclusion of trigram along with unigram and bigram achieves the best result in terms of F1-macro score and accuracy among all three classifiers. Perceptron shows the best results in comparison with Random Forest and Perceptron classifiers, with an accuracy of 0.846945 and an F1-macro score of 0.83055. Significant time was spent on the validation of our efficient approach with the existing literature.

Prediction of Arabic Legal Rulings Using Large Language Models

In the intricate field of legal studies, the analysis of court decisions is a cornerstone for the effective functioning of the judicial system. The ability to predict court outcomes helps judges during the decision-making process and equips lawyers with invaluable insights, enhancing their strategic approaches to cases. Despite its significance, the domain of Arabic court analysis remains under-explored. This paper pioneers a comprehensive predictive analysis of Arabic court decisions on a dataset of 10,813 commercial court real cases, leveraging the advanced capabilities of the current state-of-the-art large language models. Through a systematic exploration, we evaluate three prevalent foundational models (LLaMA-7b, JAIS-13b, and GPT-3.5-turbo) and three training paradigms: zero-shot, one-shot, and tailored fine-tuning. In addition, we assess the benefit of summarizing and/or translating the original Arabic input texts. This leads to a spectrum of 14 model variants, for which we offer a granular performance assessment with a series of different metrics (human assessment, GPT evaluation, ROUGE, and BLEU scores). We show that all variants of LLaMA models yield limited performance, whereas GPT-3.5-based models outperform all other models by a wide margin, surpassing the average score of the dedicated Arabic-centric JAIS model by 50%. Furthermore, we show that all scores except human evaluation are inconsistent and unreliable for assessing the performance of large language models on court decision predictions. This study paves the way for future research, bridging the gap between computational linguistics and Arabic legal analytics.

Author self-reference: A cross-linguistic/cultural and cross-disciplinary analysis

This study explored the use of author self-reference from cross-linguistic/cultural and cross-disciplinary perspectives in research articles (RAs). Based on a self-built corpus consisting of two disciplines (Computer Science and Linguistics) and two languages (Chinese and English), this study extracted the self-reference markers and compared their uses across the linguistic/cultural and disciplinary corpora. The cross-linguistic/cultural analysis showed that English RAs contain a higher frequency of first-person pronouns, while Chinese RAs tend to utilize more inanimate NPs in both disciplines. The cross-disciplinary comparison identified that English Computer Science texts are characterized by a more explicit authorial persona, while Chinese RAs display a mitigated authorial presence in the text construction. The findings contribute to our knowledge of how the specific disciplinary community and cultural conventions influence writers’ manifestation of their authorial presence.

The Kazakh Language Requires Reform of its Writing

The article provides information about modern problems of writing the Kazakh language, the importance of its role and development in the context of mass digitization using artificial intelligence technologies and computational linguistics methods. The incorrectness of the current alphabet of the Kazakh language based on the Cyrillic alphabet is proved in connection with the inclusion of Cyrillic letters in it, denoting phonemes that are not included in its sound structure. The necessity of reforming the Kazakh writing by replacing the incorrect alphabet is substantiated. Errors and contradictions are shown in the approved version of the Kazakh alphabet based on the Latin alphabet, as well as the alphabet proposed as a replacement for the approved one, in which some previous errors are repeated. In both cases, no analysis and clarification of the sound system of the Kazakh language, which is the basis of any alphabet, is carried out. In this study, to clarify the sound system of the Kazakh language, experiments were carried out to determine the articulation and acoustic features of Kazakh sounds with the help the computer programs used for many natural languages. In the articulation analysis, special attention was paid to vowels, which give rise to various contradictions in the Kazakh letter. It is proposed to use a new classification of vowels according to four binary features, rather than the traditional classification according to three binary features. Acoustic analysis uses the method of formant analysis, which is aimed at identifying certain formants in the spectrogram. The formant is obtained using a spectrograph. Quantitatively, the formants correspond to the maxima in the speech spectrum and usually appear on spectrograms as horizontal bands. After determining the composition and classification of the sound system of the Kazakh language, two variants of the alphabet based on the Latin alphabet are proposed: the first one is based on the Turkish alphabet using diacritical marks; the second is based on the English alphabet using digraphs. The second option offers ways to solve problems that arise when using digraphs. In conclusion, information is provided on the ongoing and ongoing work in Kazakhstan related to the creation of smart systems in the Kazakh language based on the methods and technologies of artificial intelligence and computational linguistics, the results of which are reflected in the list of sources.

Care Partner Engagement in Secure Messaging Between Patients With Diabetes and Their Clinicians: Cohort Study.

Patient engagement with secure messaging (SM) via digital patient portals has been associated with improved diabetes outcomes, including increased patient satisfaction and better glycemic control. Yet, disparities in SM uptake exist among older patients and racial and ethnic underserved groups. Care partners (family members or friends) may provide a means for mitigating these disparities; however, it remains unclear whether and to what extent care partners might enhance SM use. We aim to examine whether SM use differs among older patients with diabetes based on the involvement of care partner proxies. This is a substudy of the ECLIPPSE (Employing Computational Linguistics to Improve Patient-Provider Secure Emails) project, a cohort study taking place in a large, fully integrated health care delivery system with an established digital patient portal serving over 4 million patients. Participants included patients with type 2 diabetes aged ≥50 years, newly registered on the patient portal, who sent ≥1 English-language message to their clinician between July 1, 2006, and December 31, 2015. Proxy SM was identified by having a registered proxy. To identify nonregistered proxies, a computational linguistics algorithm was applied to detect words and phrases more likely to appear in proxy messages compared to patient-authored messages. The primary outcome was the annual volume of secure messages (sent or received); secondary outcomes were the length of time to the first SM sent by patient or proxy and the number of annual SM exchanges (unique message topics generating ≥1 reply). The mean age of the cohort (N=7659) at this study's start was 61 (SD 7.16) years; 75% (n=5573) were married, 15% (n=1089) identified as Black, 10% (n=747) Chinese, 12% (n=905) Filipino, 13% (n=999) Latino, and 30% (n=2225) White. Further, 49% (n=3782) of patients used a proxy to some extent. Compared to nonproxy users, proxy users were older (P<.001), had lower educational attainment (P<.001), and had more comorbidities (P<.001). Adjusting for patient sociodemographic and clinical characteristics, proxy users had greater annual SM volume (20.7, 95% CI 20.2-21.2 vs 10.9, 95% CI 10.7-11.2; P<.001), shorter time to SM initiation (hazard ratio vs nonusers: 1.30, 95% CI 1.24-1.37; P<.001), and more annual SM exchanges (6.0, 95% CI 5.8-6.1 vs 2.9, 95% CI 2.9-3.0, P<.001). Differences in SM engagement by proxy status were similar across patient levels of education, and racial and ethnic groups. Among a cohort of older patients with diabetes, proxy SM involvement was independently associated with earlier initiation and increased intensity of messaging, although it did not appear to mitigate existing disparities in SM. These findings suggest care partners can enhance patient-clinician telecommunication in diabetes care. Future studies should examine the effect of care partners' SM involvement on diabetes-related quality of care and clinical outcomes.

The Importance of Studying Spontaneous Speech in Computational Linguistics

This scientific work provides information on the importance of studying spontaneous speech in computational linguistics. Studying spontaneous speech has numerous practical implications. The ramifications of spontaneous speech analysis are extensive, ranging from improving voice assistants and speech-to-text systems to enhancing human-computer interaction. An examination of spontaneous speech in computational linguistics offers a more authentic depiction of language usage, poses difficulties for current models, and opens up fresh opportunities for enhancing the precision and adaptability of language processing systems. The integration of spontaneous speech analysis will be crucial in developing the discipline of computational linguistics as technology progresses.

Терминологическая фреймово-сетевая модель концептуально-семантической организации понятий компьютерной лингвистики

The research object is the conceptual and semantic organization of the terminological apparatus, the research subject is the logical and conceptual structure of notions denoted by terms of computational linguistics. The study aims to identify the features of the systemic organization of notions of computational linguistics based on the use of a terminological frame-network model. The paper briefly analyzes the use of the frame approach to modeling the structure of terminological systems by linguists. A terminological frame-network model of the subject area “Computational linguistics” is proposed. The study is illustrated with fragments of the terminological network of frames of the subject area “Synonymy of terms of computational linguistics” presented in the form of diagrams. The work is novel in that it provides an original definition of a terminological network of frames and is the first to formulate the rules for its construction. The research findings showed the possibility of using a terminological frame-network model to analyze the logical and conceptual structure of the subject area “Computational linguistics” and the organization of semantic term-formation processes and lexicosemantic relations in the terminology under study.

Понятность языка правосудия: опыт эмпирического исследования содержания и синтаксиса судебных решений

For many years, the primary challenges in the legal sphere regarding language in legal proceedings have been the utilisation of national languages of the republics and foreign access to justice. However, the authors of this paper hypothesise that citizens and organisations of Russian origin often require assistance in translating “legal Russian” to “everyday Russian”. This is due to the continuing syntactic complexity found in court decision language despite widespread attempts to simplify the presentation of information. The authors’ previous research into speech practices of laws and bylaws supports this hypothesis. The authors designed research questions to investigate the hypothesis of changes in the comprehensibility of the language used in court decisions over time. Additionally, they aimed to determine whether such changes depend on the type of courts, the category of cases, or the court instance level. Empirical research methods were used to examine not only whole court decisions but also their elements. Moreover, a representative sample exceeding 15,000 texts was studied. The analysis was conducted at different parts in court decisions including introduction, description, reasoning, and operation. Additional fragments such as facts, citations of legal acts, analysis of evidence, and judge’s conclusions were also studied. The methods of computational linguistics confirmed a consistent increase in the syntactic complexity of court decisions over time. According to the author’s methodology, court decisions are among the most complex legal texts. In essence, legal acts citations contribute to the bulk of these texts, making up the largest proportion (40% on average) in the descriptive and reasoning parts of the decision, compared to other semantic fragments. The research enabled the authors to compile a catalogue of detrimental speech practices used in court decisions, alongside recommendations for their rectification.