Modelling Processes of Ukrainian-Language Text Linguistic Analysis

For the effective organisation of educational processes supported by relevant intelligent learning systems, it is important to choose the right technologies that would ensure individualisation of learning, adequate perception of learning content, and the so-called “understanding” of texts in Ukrainian provided by students (description of the solution to a task, answers provided in their own words, not selected from the test answer options, questions to the system, etc.), prototyping, constant iteration during natural language text recognition and processing, and maximum reliability and efficiency of learning processes. The purpose of the article is to study and analyse various methods of natural language processing, and the concept of NLP, and to consider common problems and prospects for developing a software product for processing Ukrainian-language text in online courses that support intelligent learning systems based on it. The research methods are the main methodological approaches and technological tools for analysing natural language texts in intelligent educational systems and developing a system for supporting NLP (Natural Language Processing) technology in the linguistic analysis of texts in Ukrainian. Such methods include, in particular: systemic and comparative analyses to identify the features of intelligence and information (with elements of intellectualisation) systems; the method of expert evaluation, which involves the study of literary sources and information resources, interviews and surveys of experts, as well as the processes of developing and testing intelligent and information systems. The novelty of the study is the analysis of modern technologies for the development of online educational process support systems through the organisation of processes of perception of information provided by students in natural language, the results of which can be used in the development of their software product to support the educational process in Ukrainian, ensuring the improvement of learning efficiency through the use of NLP technology in the process of studying the relevant academic content. Conclusions. The paper analyses modern NLP methods. The analysis has led to the selection of tokenisation, normalisation, stemming and lemmatisation methods for use in intelligent learning systems in the linguistic analysis of the so-called “free” communication in the natural (Ukrainian) language of students in the process of studying the educational content of online courses. During the tokenisation of Ukrainian-language texts, we solved such problems as eliminating so-called “merged” tokens, correcting spelling mistakes, identifying common prefixes in compound words and their impact on the semantics of the corresponding lexemes, identifying common prefixes in abbreviations, and bringing words to their normal form. Lemmatisation is especially important for the Ukrainian language (with its large number of cases of nouns, adjectives, word forms, etc.) and it requires the use of specially compiled dictionaries of the subject area under consideration. In these dictionaries, word forms are presented in the forms of lemmas (i.e., nouns are presented in the nominative case).

Purpose. Natural language text processing techniques are used to solve a wide range of tasks. One of the most difficult tasks when working with natural language texts for different languages is to find certain indicators for further determining its authorship. The problem is still relevant due to the lack of a unified tool or method for working with texts in different languages. Working with texts in Ukrainian requires taking into account its peculiarities of word and sentence construction to obtain the best result. The main purpose of this article is to analyze the existing methods of text processing, their features and effectiveness in working with texts of different languages. Methodology. Natural language text processing methods are systematized by type and format, according to the tools and approaches used. For each method, its features, effectiveness, scope, and limitations are considered. The means of system analysis were used to form the final characterization of the method, taking into account its purpose and capabilities. Findings. The study of methods has revealed the following ones used for the intellectual analysis of texts in different languages, their scope, effectiveness in working with different languages, strengths and weaknesses. This will make it possible to choose an effective toolkit for working with Ukrainian texts. It has been established that intelligent text processing is a complex task that requires an individual approach to each language to take into account its peculiarities and obtain the best result. Originality. The basis for choosing an effective method for working with Ukrainian-language texts is formed, the existing methods of intellectual text processing, their application features, capabilities and efficiency in working with texts of different languages are analyzed and systematized. Practical value. The work allowed us to identify the most promising, effective and appropriate methods of intellectual analysis of natural language texts in order to use them for processing Ukrainian-language texts in the future.

This contribution outlines the dispositif approach, which combines a linguistic discourse analysis of texts with a sociological study of the social context (i.e. the dispositif understood as an institutional arrangement of practices and structures). The authors use the discourse of academic researchers to exemplify this approach. By articulating correspondence analysis of self-representations on researchers’ homepages with institutional data of sociology professors in the United Kingdom, they outline a research design that consists of three components: a linguistic analysis of texts, a sociological analysis of institutional contexts, and a theoretical account of how the two are related in the academic dispositif. The dispositif perspective on discourse aims to respond to a demand for systematic discourse research on the social and institutional contexts of discursive practices.

<p style="text-align: justify;">The work is devoted to verbal personification as a means of predicate evaluation, which is in demand when analyzing the concept of «GULAG» in A.I. Solzhenitsyn's novel «The GULAG Archipelago». The purpose of the article is to explore verbal personification and ways of expressing it, which are important for the writer to create an image of the GULAG. The object of the study is the text of A.I. Solzhenitsyn's novel «The GULAG Archipelago», the subject is the verb as a means of personification. Description of the procedure and research methods. The text of A.I. Solzhenitsyn's novel «The GULAG Archipelago» examines the role of verbal personification in creating the image of Stalin's camps and the representation of the concept of «GULAG». Methods of observation and comparison, elements of component analysis, contextual, lexicographic analysis are applied. The results of the study: one of the main ways to create an image of the GULAG in the novel by A.I. Solzhenitsyn — verbal personification — was identified and received a multidimensional assessment from a linguistic point of view. It is proved that personifiers become a means of creating an image of the Stalinist camps and its inhabitants (prisoners), which is part of the scope of the concept and image of the «GULAG» and is of interest for linguistic research. Practical/theoretical significance of the study. The results of the research contribute to the study of the language of A.I. Solzhenitsyn's works, expand the understanding of the writer's conceptual sphere and the means of imagery of his texts; they can be applied in the development of the disciplines «Language of fiction», «Stylistics», «Linguistic analysis of text», «Linguistic analysis of literary text» at the university.</p>

The development of information (intelligent) learning systems, electronic document management systems, web-oriented systems working with text information in natural language has led to an increase in the volume of educational content and/or arrays of processed full-text documents. All this requires new means of organizing access to information, many of which should be classified as intelligent systems for knowledge processing. One of the effective approaches to identifying and processing the meaning of educational content (and/or text documents) is the use of ontologies. The purpose of the article is research, analysis of various approaches to determining the semantic content of texts in natural language, consideration of existing concepts of text analysis and prospects for using the proposed ontological approach to semantic analysis of texts in natural language. Research methods are methods of semantic analysis of the main concepts of the analyzed subject area (semantic analysis of texts in natural language). The article considers an approach to the linguistic analysis of texts based on ontological modeling. The novelty of the research is the application of the proposed ontological approach to the semantic analysis of texts in natural language to determine the meaning (semantics) of text information, which is used in intelligent systems of various classes. The conclusion of the research carried out in the article is as follows: an ontological approach to the semantic analysis of natural language text, its tasks and methods is proposed. The use of the proposed approach to text analysis leads to the understanding of semantic analysis as a single triad: <Ontology – Text – Meaning>. For effective and correct extraction of knowledge, it is suggested to use a multi-level ontology. The result of the interaction of a specific natural language text with an ontology is an ontological meaning – a set of interconnected subgraphs of the ontograph. The ontological content is extracted from the ontograph using a semantic analyzer. The dialogue processor examines the syntactic tree of sentence parsing (a connected element of the natural language text) and, based on the given question, finds a fragment in the text that is the answer to the question. Computer understanding (in information or intellectual systems) of natural language text is achieved, in particular, by: immersion of the text in a single environment of knowledge – ontology; formal presentation of meaning (semantics) in the knowledge base of the corresponding system; the possibility of operations on the ontological content. The proposed approach can be used to create intelligent information repositories that work in a single knowledge environment. The proposed approach to the semantic analysis of texts in natural language is focused on the automatic extraction of metadata from texts of various nature (for example, a text document, Internet content, educational content of relevant online courses, description of computer and board games). With further development of the proposed approach, it can be used in systems of automatic referencing of scientific publications, meaningful interpretation of multimedia content, training and testing (including elements of visual display of information and elements of gamification).

Artificial intelligence is beginning to spread to all fields of science, and philology, linguistics and literature are no exception. This intersection of technology and the humanities opens a new era of linguistic and literary analysis. The relevance lies in the fact that artificial intelligence offers innovative ways of understanding and interpreting texts. Linguistic analysis of texts is an important part of philological research, as it helps to uncover the meaning, style, and other elements of language use in texts. This study aims to explore the innovative technology of Salesforce Einstein Artificial Intelligence, which can be used for linguistic text analysis in philological research. Research methods include literature review, correlation analysis, social analysis, impact analysis, syntactic analysis, and factor analysis. The results of the work showed that the system deeply learns the language and learns to recognize and analyze various language features and emotions. The paper examines the technical and ethical aspects of Salesforce Einstein AI. A cross-sectional study allowed us to describe the technological capabilities of the system and to debate the problem of replacing the author with artificial intelligence from an ethical perspective. The authors conclude that linguistic text processing in philology has revolutionized the way authors interact with technology and has become a valuable tool in text analysis. Salesforce Einstein AI has opened up a whole new world of opportunities for the field of philology.

IMPORTANCE. The relevance of a comparative analysis of A.S. Pushkin’s poems “I loved you” and “On the hills of Georgia” lies in the methodology application of linguistic analysis of literary text. This approach is inherently comprehensive, enabling the most effective reflection of interdisciplinary connections and knowledge within the framework of a multi-level language system. The aim of the research is to use linguistic analysis to demonstrate the potential for interpreting Pushkin’s texts, taking into account their thematic and genre proximity, specifics of compositional structure, and imagery. Special attention is paid to the functional sentence perspective (actual division), which is often not considered a component of linguistic analysis of literary text.MATERIALS AND METHODS. The texts of A.S. Pushkin are presented as linguistic material. Descriptive and textual methods are used.RESULTS AND DISCUSSION. Research results confirm scholars’ conclusions about the simplicity of language in Pushkin’s texts as a characteristic feature of the poet’s individual style during his late creative period. The most significant trait, playing a considerable role in creating the overall imagery, is inversion, which exhibits proximity to colloquial literary speech. The study has identified semantic loads of grammatical components within utterances, demonstrating varying relationships with functional sentence perspective (actual division). Consequently, it has been established that functional sentence perspective facilitates the semantic development of the text, structures meaningful connections between utterances, and ultimately ensures a holistic comprehension of the text through their unity.CONCLUSION. The analysis of A.S. Pushkin’s poems has demonstrated the necessity of incorporating into the linguistic analysis of literary text not only its traditionally recognized components (the text’s history of creation, its themes and genre, imagery, and figurative-expressive devices) but also such an element as functional sentence perspective (actual division). This component is systematically essential for understanding the text as a whole and enables the unity comprehension between linguistics and literary studies in the reader’s perception of a literary text as an aesthetic object.

Business Process Management (BPM) has been receiving increasing attention in recent years. Many organizations have been adapting their business to a process-centered view since they started noticing its potential to reduce costs, improve productivity and achieve higher levels of quality. However, implementing BPM in organizations requires time, making the automation of process identification and discovery highly desirable. To achieve this expectation, the application of Natural Language Processing (NLP) techniques and tools has emerged to generate process models from unstructured text. In this paper, we provide the results of a systematic literature review conducted in preparation and processing of natural language text aiming the extraction of business processes and process quality assurance. The study presents techniques applied to the BPM life-cycle phases of process identification, process discovery and process analysis as well as tools to support process discovery. This review covered papers from 2009 up to 2016 and identifies 518 articles of which 33 were selected as relevant to our work. The results of the present study may be valuable to support research in extraction of business process models from natural language text.

The paper deals with the issue of linguistic analysis of English language texts on the basis of textuality standards which allow to carry out a complete analysis of the text and judge about its communicative value. The appropriateness of linguistic analysis of texts with the consideration of seven standards of textuality have been substantiated. These standards are: coherence (introduction of a new item in the text and subsequent referral to it by means of another item), cohesion (the ways in which components of the sentences in a text are mutually connected grammatically and lexically), intentionality and acceptability (a speaker/author’s intention to produce a sound piece of information and the recipient’s desire to accept the text as a communicative piece of information), informativity (ways and means by which parts of the text acquire communicative value), contextuality (focusing on the context and the role it plays in any form of communication) and intertextuality (understanding and perception of one text on the basis of the structure of another text similar to it). Special attention has been paid to cohesion as semantic relation between elements of the text. Categories of cohesion such as reference, substitution, ellipsis, conjunction and lexical cohesion have been analyzed. It has been defined that the linguistic analysis carried out with consideration of seven standards of textuality requires knowledge of different branches of linguistics – syntax, morphology, phonology, grammar, semantics, lexicology, stylistics, pragmatics, sociolinguistics. Definite examples of every standard application have been presented. It has been substantiated that the appropriateness of a text is the agreement between its setting and the ways in which the standards of textuality are applied. The standards of textuality entail factors of cognition, planning and social environment merely to distinguish what constitutes a text.

The article studies different aspects of linguistic and stylistic analysis of authentic literary text. The author defines such notions as “text” “literary text”, “authentic text”, “linguistic and stylistic analysis”, reveals various approaches to the analysis of authentic literary text and studies the procedure used for teaching Orenburg State University students who specialize in foreign languages and foreign literature how to carry out a relevant and perfect linguistic and stylistic analysis. The author treats an authentic text as a text which native speakers produce for native speakers to perform a certain social function in the linguistic community where it has been produced considering peculiarities of the national culture. Linguistic and stylistic analysis of authentic literary text is defined in the article as a method aimed at studying linguistic means and stylistic devices of different levels according to the functional and aesthetic point of view as well as from the point of view of their correspondence to author’s intention, his individual manner of writing. The author proves the validity of the methodology based on the integration of literary; linguistic; psycholinguistic and communicative approaches (considering psycholinguistic approach as a leading one) and the procedure including preparatory, analytical and reflexive stages. This conclusion has been proved by the results of observations and students’ tests.

The paper reviews the monograph “Theory and Methods of Linguistic Analysis of Political Text” (executive editor A.P. Chudinov, Ekaterinburg, 2016). The aim of the book under review is to determine the object, subject, theory and methodology of political linguistics, and its place among the interconnected disciplines. The monograph consists of three chapters: “General Questions of Political Linguistics”, “Methodology and Methods of National Political Linguistics Studies”, and “Methodology and Methods of Political Communication Studies Abroad”. The first chapter defines the status of political linguistics and other “hybrid” linguistics, examines the question of political linguistics methodology, and reviews the activities of the scientific school “Political Linguistics”. The second chapter describes the areas of focus in the political linguistics: semiotic analysis, linguistic and pragmatic analysis, genre analysis, linguistic political prognostics, study of historical dynamics of metaphorical systems, comparative analysis of political texts, and linguistic and political personology. The third chapter presents a review of the leading methodological approaches to the study of political communication in foreign linguistics: cognitive methodology, rhetorical methodology, critical discourse analysis, psycholinguistic methodology, content analysis, and corpus linguistics (quantitative methods). The monograph under review allows the author to make the conclusion that theory and methods of linguistic analysis of political text can make a claim for a scientific discipline with its own research and conceptual apparatus, being connected with other branches of linguistics at that.

1 IntroductionOrganizations focus on automate their processes in order to improve efficiency, reduce costs, and/or reduce human beings' errors in an easy and rapid manner. Business process management (BPM) methods provide a solution to this issue. In this context, information systems like CRM, ERP, SCM, etc. have known an increasing demand. The main problem consists of the length of the business process specifications. If new regulations appear, these specifications must be adapted. Manually extraction of visual models is time consuming. During time, a series of solutions were proposed. The literature shows a crowd of approaches that extracts data models [1], [2], [3], and process models [4], [5] from Natural Language (NL) text. This paper aims to analyze the Natural Language Processing (NLP) techniques and tools used in order to provide different types of visual representations.On the last few years, approaches based on NLP have been developed in order to automate process conversion from NL text. NLP plays an important role in NL text analysis as NLP tries to understand speech and text as humans beings would do. Colloquialism, abbreviations or typos make this task a challenging one. NLP has the origins in 1950s when Alan Turing proposed the Turing test [6], by introducing the imitation game. Since then, the literature shows a plethora of NLP tools [7], [8], [9], [10] using several machine learning techniques, our focus being on those applied on data models and process models discovery from NL text, starting with the first language parser [11] to the actual ones like NLTk [7], [8], ANTLR1, etc.Linguistic analysis is closely tied to NLP. Liddy [12] highlights 7 levels of linguistic analysis: a) Phonetic or Phonological level: how words are pronounced, b) Morphological level: prefixes, suffixes and roots analysis, c) Lexical level: word level analysis including lexical meaning and Part-Of-Speech (POS) analysis, d) Syntactic level: grammatical analysis of words in a sentence, e) Semantic level: determining the possible meanings of sentences, f) Discourse level: interpreting structure and meaning for texts larger than a sentence, g) Pragmatic level: understanding the purpose of a language.Some of the problems approached by NLP are: POS tagging, parsing, Named Entity Recognition (NER), chunking, Semantic Role Labeling (SRL). Anaphora resolution [13] refers to the interpretation of the link between the anaphor and its antecedents.The remainder of the paper is organized as follows: Section 2 briefly outlines the NLP domain, describing the levels of linguistic analysis and the main NLP approaches of analyzing NL requirements. Section 3 focuses on data and process models extraction from NL text. This section makes an introduction to Object Oriented Analysis and Business Process Modeling and analyses the existing tools that discover data and process models from text. Subsequently, Section 4 summarizes the results of this work and the conclusions are drawn in Section 5.2NLPA detailed review on NLP is given in [14] and in [15]. Jones [14] divides the history of NLP into four phases: the first starts at the beginning of the 1940s and lasts to the late 1960s, the second begins from the end of 60s and lasts to the end of 70s, the third is represented by late 80s, where the fourth phase starts in the late of 90s. Next, we will detail each phase as they were defined in [14] and [15]. First phase treated machine translation issues, while the second focused on artificial intelligence. The third phase can be called grammatico-logical phase, which is followed by the lexical phase. A fifth phase is proposed in [16] where formal theories and statistical data are combined. Since this study was published first in 1994 and then it was re-organized in 2001 we can add the sixth phase: from 2000 until present where NLP techniques are combined in order to contribute to visual models extraction.Software requirements are usually written in NL which is asymmetric and irregular [17]. …

Emdros is a text database engine for linguistic analysis or annotation of text. It is appliccable especially in corpus linguistics for storing and retrieving linguistic analyses of text, at any linguistic level. Emdros implements the EMdF text database model and the MQL query language. In this paper, I present both, and give an example of how Emdros can be useful in computational linguistics.

This article represents approaches of the artificial intelligence methods, used in public administration. An overview of various technologies of artificial intelligence applications in the field of public administration and related fields is given. All of these research directions are particularly relevant to the task of digital technologies (including artificial intelligence) growth to create an efficient and competitive digital economics in Russia. Among the modern intellectual technologies that allow solving the widest range of tasks, an important role plays technologies related to natural language text processing - nonstructured NL-texts are essential segment of data, used for analysis and decision-making tasks (in terms of data volume, of course, video data have a leading position, however, they are usually suitable for solving tactical but not strategic management tasks). The article provides an overview of the existing methods of natural language processing and their practical application to the tasks of public administration. An integrated approach to the natural language processing tools using for solving practical problems in the field of public administration is considered on the example of the ISIDA-T system for extracting information from natural language texts, developed at the Artificial Intelligence Research Center PSI RAS. The system under consideration is distinguished by a modular approach to the pre-processing of unstructured text and the possibility of manual adjustment for a specific extraction task. This technological solution gives the necessary flexibility and ease of use. The system consists of configurable text preprocessing, linguistic analysis, target information retrieval and output of the results in user-friendly form modules. One of the important components of the system is an integrated knowledge resource that allows quickly and efficiently adjust the system to the specifics of the relevant subject area. An approach to the application of the ISIDA-T system to the task of fact information extraction (as the most important for analyzing the situation and subsequent management decisions) is proposed using the example of information extraction about resignations and appointments from news feeds.

Recently, learning approaches have utilized Artificial Intelligence (AI) and the Internet of Things (IoT) to create an efficient learning environment. The application of IoT and AI to improve learning systems is thoroughly examined in this paper. Additionally discussed are various IoT and AI-based approaches and strategies related to e-learning, M-learning, methods employed, and particular applications. A new era of linguistic and literary analysis is ushered in by the convergence of technology and the humanities. The creative ways that artificial intelligence provides for comprehending and interpreting texts make it relevant. As it enables the discovery of meaning, style, and other aspects of language usage in texts, linguistic analysis of texts is a crucial component of philological investigation. In this human-centric paradigm, it also becomes imperative to investigate how AI aligns with human values. In particular, giving in-service teachers examples of how to apply the suggested framework improved their understanding of generative AI concepts and how to incorporate them into their instruction. To handle learning techniques more effectively, the results of this evaluation will guide the creation of strategies that combine IoT and AI.