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Journal articles on the topic 'Natural language texts'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

Kreydlin, Grigory. "Paralanguage in Natural Language Texts." Russkaia rech, no. 1 (2023): 44–65. http://dx.doi.org/10.31857/s013161170024705-4.

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In oral communication natural language is always combined with nonverbal signs of various nature, in particular paralanguage signs. The present paper describes usages and functions of lexical paralanguage units in Russian texts, mainly texts of anecdotes, where these units serve as a key to comic effect. The article analyzes examples of nonverbal imitation of speech pathology and ethnic, social, professional and other peculiarities of human communicative behavior. The paper describes some functions of paralinguistic signs such as their ability to imitate specific acts of pronunciation, to substitute taboo speech units or to denote other real actions. The authors pay attention to the form, meaning and usage of paralanguage units in Russian. Some of the units regarded are supplied with semantic descriptions and with descriptions of spheres of their usage. The article establishes syntactic function of paralanguage units in dialogues. Paralinguistic units constitute the class of so-called signalatives, which do not denote propositions but indicate peoples’ emotions or their mental activity

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Belonogov, G. G., A. A. Khoroshilov, and A. A. Khoroshilov. "Phraseological computer-aided translation of natural language texts into other natural languages." Automatic Documentation and Mathematical Linguistics 44, no. 5 (2010): 262–64. http://dx.doi.org/10.3103/s0005105510050055.

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3

Korshunov, Anton, and Andrey Gomzin. "Topic modeling in natural language texts." Proceedings of the Institute for System Programming of RAS 23 (2012): 215–44. http://dx.doi.org/10.15514/ispras-2012-23-13.

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Shamsfard, Mehrnoush, and Ahmad Abdollahzadeh Barforoush. "Learning ontologies from natural language texts." International Journal of Human-Computer Studies 60, no. 1 (2004): 17–63. http://dx.doi.org/10.1016/j.ijhcs.2003.08.001.

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Piotrowski, Michael. "Natural Language Processing for Historical Texts." Synthesis Lectures on Human Language Technologies 5, no. 2 (2012): 1–157. http://dx.doi.org/10.2200/s00436ed1v01y201207hlt017.

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6

Connolly, John H. "Interpreting anaphors in natural language texts." Knowledge-Based Systems 2, no. 3 (1989): 191. http://dx.doi.org/10.1016/0950-7051(89)90024-5.

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Kimura, Daisuke, and Kumiko Tanaka-Ishii. "Study on Constants of Natural Language Texts." Journal of Natural Language Processing 21, no. 4 (2014): 877–95. http://dx.doi.org/10.5715/jnlp.21.877.

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Papadimitriou, C., K. Karamanos, F. K. Diakonos, V. Constantoudis, and H. Papageorgiou. "Entropy analysis of natural language written texts." Physica A: Statistical Mechanics and its Applications 389, no. 16 (2010): 3260–66. http://dx.doi.org/10.1016/j.physa.2010.03.038.

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Chan, Ki, and Wai Lam. "Extracting causation knowledge from natural language texts." International Journal of Intelligent Systems 20, no. 3 (2005): 327–58. http://dx.doi.org/10.1002/int.20069.

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10

Borisov, E. S. "Self-training classifier of natural-language texts." Cybernetics and Systems Analysis 43, no. 3 (2007): 455–61. http://dx.doi.org/10.1007/s10559-007-0070-6.

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Shynkarenko, Viktor, and Inna Demidovich. "CONSTRUCTIVE-SYNTHESIZING MODELING OF NATURAL LANGUAGE TEXTS." Computer systems and information technologies, no. 3 (September 29, 2023): 81–91. http://dx.doi.org/10.31891/csit-2023-3-10.

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Means for solving the problem of establishing the natural language texts authorship were developed. Theoretical tools consist of a constructors set was developed on the basis of structural and production modeling. These constructors are presented in this work. Some results of experimental studies based on this approach have been published in previous works by the author, the main results should be published in the next ones. Constructors developed: converter of natural language text into tagged, tagged text into a formal stochastic grammar and the authors style similarity degree establishment of two natural language works based on the coincidence of the corresponding stochastic grammars (their substitution rules). The proposed approach makes it possible to highlight the semantic features of the author's phrases construction, which is a characteristic of his speech. Working with a sentence as a unit of text to analyze its construction will allow you to more accurately determine the author's style in terms of the use of words, their sequences and characteristic language constructions. Allows not to be attached to specific parts of speech, but reveals the general logic of building phrases.

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Namiq oğlu Talıbov, Tale. "Ontological processing on retrieval information from natural language texts." NATURE AND SCIENCE 20, no. 5 (2022): 29–34. http://dx.doi.org/10.36719/2707-1146/20/29-34.

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Təbii dil mətnlərinin emalı və ya orijinal adı ilə Natural Language Processing (NLP), süni intellektin inkişafı və dilçiliklə birgə edilmiş araşdırmalar nəticəsində həyatımıza daxil olmuş bir termindir. Başqa cür desək, təbii dil mətnlərinin emalı Azərbaycanca, Rusca, İngiliscə kimi təbii dillərdəki mətnlərin, səs dalğalarının kompüter tərəfindən mənimsənilərək, müxtəlif proqramlarda təhlil edilməsi və kompüter mühitinə köçürülməsidir. Hər kəsin bildiyi kimi, təbii dil insanların ünsiyyət və həyatda qalmaq üçün istifadə etdiyi ən əsas xüsusiyyətlərdən biridir. Açar sözlər : təbii dil mətnlərinin emalı, süni intellekt, maşın tərcüməsi, semantik etiketlənmə, ontologiya Tale Namiq Talibov Ontological processing on retrieval information from natural language texts Abstract Natural Language Processing (NLP) is a term that has entered our lives as a result of the development of artificial intelligence and research combined with linguistics. In other words, the processing of natural language texts is the computer adoptering of texts and sound waves into the natural languages such as Azerbaijani, Russian and English, their analysis in various programs and their transfer to the computer environment. As we all know, natural language is one of the most important features that people use to communicate and survive. Similarly, speaking is a feature of language that occurs in all areas of our lives and is easier to express than writing. In fact, people can control all their work with voice and text. The processing of natural language texts, including our lives, will give us many advantages in doing all our work. Key words: Natural Language Processing , Machine Translation, Word Processing, Text Processing, Text Summarization, Argument Aggregation

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Baud, R. H., A. M. Rassinoux, and J. R. Scherrer. "Natural Language Processing and Semantical Representation of Medical Texts." Methods of Information in Medicine 31, no. 02 (1992): 117–25. http://dx.doi.org/10.1055/s-0038-1634865.

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Abstract:For medical records, the challenge for the present decade is Natural Language Processing (NLP) of texts, and the construction of an adequate Knowledge Representation. This article describes the components of an NLP system, which is currently being developed in the Geneva Hospital, and within the European Community’s AIM programme. They are: a Natural Language Analyser, a Conceptual Graphs Builder, a Data Base Storage component, a Query Processor, a Natural Language Generator and, in addition, a Translator, a Diagnosis Encoding System and a Literature Indexing System. Taking advantage of a closed domain of knowledge, defined around a medical specialty, a method called proximity processing has been developed. In this situation no parser of the initial text is needed, and the system is based on semantical information of near words in sentences. The benefits are: easy implementation, portability between languages, robustness towards badly-formed sentences, and a sound representation using conceptual graphs.

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Kotov, Yurii, and Olga Sanina. "Generating pseudo-random texts based on the frequency characteristics of texts in natural languages." Transaction of Scientific Papers of the Novosibirsk State Technical University, no. 1-2 (August 26, 2020): 113–26. http://dx.doi.org/10.17212/2307-6879-2020-1-2-113-126.

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The paper discusses generation of pseudo-random texts based on frequency characteristics of texts in natural languages. The follow frequency characteristics of texts and their values for the Russian and English languages are considered for generation: the distribution of unigrams and bigrams over frequency of occurrence in texts, the distribution of words over the length. Based on the considered frequency characteristics, an algorithm for generating pseudo-random texts is suggested. Texts generated according to the algorithm are studied in experiments of language recognition in texts.

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Gromov, Vasilii A., and Anastasia M. Migrina. "A Language as a Self-Organized Critical System." Complexity 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/9212538.

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A natural language (represented by texts generated by native speakers) is considered as a complex system, and the type thereof to which natural languages belong is ascertained. Namely, the authors hypothesize that a language is a self-organized critical system and that the texts of a language are “avalanches” flowing down its word cooccurrence graph. The respective statistical characteristics for distributions of the number of words in the texts of English and Russian languages are calculated; the samples were constructed on the basis of corpora of literary texts and of a set of social media messages (as a substitution to the oral speech). The analysis found that the number of words in the texts obeys power-law distribution.

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Kehl, Walter, Mike Jackson, and Alessandro Fergnani. "Natural Language Processing and Futures Studies." World Futures Review 12, no. 2 (2019): 181–97. http://dx.doi.org/10.1177/1946756719882414.

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Because the input for Futures Studies is to a very high degree formulated as written words and texts, methods which automate the processing of texts can substantially help Futures Studies. At Shaping Tomorrow, we have developed a software system using Natural Language Processing (NLP), a subfield of Artificial Intelligence, which automatically analyzes publicly available texts and extracts future-relevant data from theses texts. This process can be used to study the futures. This article discusses this software system, explains how it works with a detailed example, and shows real-life applications and visualizations of the resulting data. The current state of this method is just the first step; a number of technological improvements and their possible benefits are explained. The implications of using this software system for the field of Futures Studies are mostly positive, but there are also a number of caveats.

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Demidovich, I. M. "Methods of Intellectual Text Analysis." Science and Transport Progress, no. 3(103) (September 29, 2023): 31–43. http://dx.doi.org/10.15802/stp2023/295252.

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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.

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Kimura, Daisuke, and Kumiko Tanaka-Ishii. "A Study on Constants of Natural Language Texts." Journal of Natural Language Processing 18, no. 2 (2011): 119–37. http://dx.doi.org/10.5715/jnlp.18.119.

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19

Legnar, Maximilian, Philipp Daumke, Jürgen Hesser, et al. "Natural Language Processing in Diagnostic Texts from Nephropathology." Diagnostics 12, no. 7 (2022): 1726. http://dx.doi.org/10.3390/diagnostics12071726.

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Introduction: This study investigates whether it is possible to predict a final diagnosis based on a written nephropathological description—as a surrogate for image analysis—using various NLP methods. Methods: For this work, 1107 unlabelled nephropathological reports were included. (i) First, after separating each report into its microscopic description and diagnosis section, the diagnosis sections were clustered unsupervised to less than 20 diagnostic groups using different clustering techniques. (ii) Second, different text classification methods were used to predict the diagnostic group based on the microscopic description section. Results: The best clustering results (i) could be achieved with HDBSCAN, using BoW-based feature extraction methods. Based on keywords, these clusters can be mapped to certain diagnostic groups. A transformer encoder-based approach as well as an SVM worked best regarding diagnosis prediction based on the histomorphological description (ii). Certain diagnosis groups reached F1-scores of up to 0.892 while others achieved weak classification metrics. Conclusion: While textual morphological description alone enables retrieving the correct diagnosis for some entities, it does not work sufficiently for other entities. This is in accordance with a previous image analysis study on glomerular change patterns, where some diagnoses are associated with one pattern, but for others, there exists a complex pattern combination.

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Voloshyn, O. F., L. O. Sviatogor, and V. V. Morgun. "Semantic Analysis of Texts Presented in Natural Language." Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, no. 1 (2019): 228–33. http://dx.doi.org/10.17721/1812-5409.2019/1.53.

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The article considers a cognitive approach to one of the problems of artificial intelligence consisting in semantic analysis of texts presented in natural language. Development of methods and means of discourse analysis is a necessary step for building systems of intellectual communication between a person and a computer (robot). A scientific hypothesis is proposed suggesting that an observer of the environment studies and understands the World when he is able to highlight and analyze Facts, Events and Situations. Corresponding linguistic and cognitive structures of these "units of semantic analysis" of the reality - "Fact", "Event", "Situation" and "Sense" - are proposed and justified. The Sense is considered a prerequisite for understanding dialogue and discourse.

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Tkachenko, Kostiantyn. "Semantic Analysis of Natural Language Texts: Ontological Approach." Digital Platform: Information Technologies in Sociocultural Sphere 7, no. 2 (2024): 211–23. https://doi.org/10.31866/2617-796x.7.2.2024.317726.

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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).

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Бершадский, А., П. А. Гудков, and Е. М. Подмарькова. "Bottom-up syntax analysis for natural language texts." МОДЕЛИРОВАНИЕ, ОПТИМИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ 9, no. 1(32) (2021): 1–2. http://dx.doi.org/10.26102/2310-6018/2021.32.1.001.

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Актуальность работы обусловлена необходимостью автоматизации процесса принятия решений по юридическим вопросам в различных областях человеческой деятельности. В связи с этим, данная статья направлена на раскрытие подхода к организации процесса синтаксического анализа текстов на естественном языке для последующего автоматического построения семантической сети в соответствии с заданными входными документами. В качестве предметной области рассматривается сфера правовой информации. Предлагаемый авторами подход открывает широкие возможности по смысловому анализу правовых документов и их сравнении между собой. В статье приводится алгоритм восходящего синтаксического разбора. Результаты работы рассмотренного алгоритма применимы для последующего формирования базы знаний по имеющимся текстам правовых документов. В качестве модели представления знаний предполагается использовать семантические сети, что открывает широкие перспективы по автоматизации обработки правовой информации. Помимо решения часто встречающихся на практике задач принятия решений по юридическим вопросам, рассмотренный подход позволит автоматизировать решение такой трудоёмкой задачи, как автоматизация проведения юридической экспертизы нормативно-правовых актов. Проведение этой процедуры необходимо для того, чтобы принимаемые нормативные правовые акты соответствовали принципам допустимости и правомерности включения их в действующую систему права. The need to automate the decision-making process on legal issues in various fields of human activity determines the relevance of this work. In this regard, this article is aimed at disclosing an approach to organizing the process of parsing texts in natural language for the automatic construction of a semantic network corresponding to the given input documents. The subject area is the field of legal information. The approach proposed by the authors opens up wide possibilities for the semantic analysis of legal documents and their comparison with each other. The article discusses the organization of the process of bottom-up parsing natural language texts for the further automatic building a semantic network. The authors propose the text parsing algorithm. Its results are applicable for the further formation of the knowledge base on the available texts of legal documents. Semantic networks are supposed to be used as a model for representing knowledge, which opens up broad prospects for the automation of legal information processing. In addition to solving the problems of making decisions on legal issues that are often encountered in practice, the considered approach will automate the solution of such a time-consuming task as the automation of the legal examination of regulatory legal acts. The implementation of this procedure is necessary in order for the adopted regulatory legal acts to comply with the principles of admissibility and legality of their inclusion in the current system of law.

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Lippi, Marco, Marcelo A. Montemurro, Mirko Degli Esposti, and Giampaolo Cristadoro. "Natural Language Statistical Features of LSTM-Generated Texts." IEEE Transactions on Neural Networks and Learning Systems 30, no. 11 (2019): 3326–37. http://dx.doi.org/10.1109/tnnls.2019.2890970.

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Gallois, Cynthia, and Jeffery Pittam. "Social Psychological Approaches to Using Natural Language Texts." Journal of Language and Social Psychology 14, no. 1-2 (1995): 5–17. http://dx.doi.org/10.1177/0261927x95141001.

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Pimeshkov, Vadim K., and Maxim G. Shishaev. "Methods of knowledge extraction from natural language texts." Transactions of the Kоla Science Centre of RAS. Series: Engineering Sciences 13, no. 2/2022 (2022): 31–45. http://dx.doi.org/10.37614/2949-1215.2022.13.2.003.

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The paper considers methods for knowledge extraction from natural language texts. A formal definition of the task is given, two main subtasks are distinguished: concept extraction and relationship extraction. The classification of methods is considered from the point of view of the language and language resources, from the point of view of setting the problem, as well as from the point of view of solving problems of extracting concepts and relations.

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Souter et al., Clive. "Natural Language Identification using Corpus-Based Models." HERMES - Journal of Language and Communication in Business 7, no. 13 (2017): 183. http://dx.doi.org/10.7146/hjlcb.v7i13.25083.

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This paper describes three approaches to the task of automatically identifying the language a text is written in. We conducted experiments to compare the success of each approach in identifying languages from a set of texts in Dutch/Friesian, English, French, Gaelic (Irish), German, Italian, Portuguese, Serbo-Croat and Spanish.....

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Palagin, A. V., M. G. Petrenko, and D. G. Zelentsov. "On the problem of computer processing of natural language texts." Computer Modeling: Analysis, Control, Optimization 7, no. 1 (2020): 37–45. http://dx.doi.org/10.32434/2521-6406-2020-1-7-37-45.

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The present paper deals with the general approach to the problem of analyzing natural language information, including the implementation of a number of information technologies related in one way or another to language modeling. In addition to the development of the aforementioned tech-nologies, it is necessary to develop a formal theory of computer processing of knowledge extracted from natural language texts. The specific features of constructing linguistic models and the criteria for understanding natural language texts are analyzed. This raises a number of problems. The first problem comes down to the problem of analyzing textual information presented in natural language (morphological, syntactic, semantic and logical analysis) in order to extract knowledge. The second problem is associated with designing a system for searching, processing and extracting knowledge, developing and constructing its architecture, as well as tools for the user. The third problem is the development of procedures for the integration of knowledge from several subject areas to ensure the effectiveness of conducting studies of an interdisciplinary and transdisciplinary nature. It is also necessary to pay special attention to the use of already developed theoretical prin-ciples and practical solutions. A formal statement of the problem of the analysis of natural language texts is proposed, in which the main subtasks are identified, associated with the calculation of typ-ing relationships of vocabulary of a natural language on a lexico-semantic continuum and the inter-pretation of some text on a given subject model. In the context of the developed architecture of the linguistic-ontological information system, a formal model for processing natural-language texts is proposed, for which an unambiguous correspondence of the processes of processing natural-language information and means (architectural blocks) of their implementation is shown. Keywords: ELRE natural language text, linguistic model, language ontological information system, analysis and understanding of natural language texts.

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Sarukkai, Sundar. "Mathematics, Language and Translation." Meta 46, no. 4 (2002): 664–74. http://dx.doi.org/10.7202/004032ar.

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Abstract The mathematical discourse is not possible without a fertile use of natural language. Its symbols, first and foremost, refer to natural language terms. Its texts are a combination of symbols, natural language, diagrams and so on. To coherently read these texts is to be involved in the activity of translation. Applied mathematics, as in physics, constantly shifts from one language (and culture) to another and, therefore, is best understood within the ambit of translation studies.

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Hkiri, Emna, Souheyl Mallat, and Mounir Zrigui. "Events Automatic Extraction from Arabic Texts." International Journal of Information Retrieval Research 6, no. 1 (2016): 36–51. http://dx.doi.org/10.4018/ijirr.2016010103.

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The event extraction task consists in determining and classifying events within an open-domain text. It is very new for the Arabic language, whereas it attained its maturity for some languages such as English and French. Events extraction was also proved to help Natural Language Processing tasks such as Information Retrieval and Question Answering, text mining, machine translation etc… to obtain a higher performance. In this article, we present an ongoing effort to build a system for event extraction from Arabic texts using Gate platform and other tools.

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Şahin qızı Kərimbəyli, Turan. "Methods for selecting authentic texts in accordance with the language level of students while teaching German." SCIENTIFIC WORK 69, no. 08 (2021): 32–37. http://dx.doi.org/10.36719/2663-4619/69/32-37.

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Today our main goal is to use authentic texts, including the study of the peculiarities of intercultural communication in the environment of communicative teaching of foreign languages. Authentic text reflects the use of natural language. It should be noted that the teaching of authentic texts in teaching a foreign language should be determined by the language level of the students. The selection criteria for authentic texts in German differ depending on the language level of the students. Key words: authentic texts, intercultural communication, communicative learning

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Qian, Longwei. "Ontology-Based Knowledge Acquisition Method for Natural Language Texts." Digital Transformation 29, no. 1 (2023): 57–63. http://dx.doi.org/10.35596/1729-7648-2023-29-1-57-63.

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The main task of knowledge acquisition (also named knowledge extraction) from natural language texts is to extract knowledge from natural language texts into fragment of knowledge base of intelligent system. Through the induction of the related literature about knowledge acquisition at a home country and abroad, this paper analyses the strengths and weaknesses of the classical approach. After emphatically researching the rulebased knowledge extraction technology and the method of building ontology of linguistics, this article proposes a solution to the implementation of knowledge acquisition based on the OSTIS technology. The main feature of this solution is to construct a unified semantic model that is able to utilize ontologies of linguistics (mainly, syntactic and semantic aspect) and integrate various problem-solving models (e. g., rule-based models, neural network models) for solving knowledge extraction process from natural language texts.

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Androutsopoulos, I., G. Lampouras, and D. Galanis. "Generating Natural Language Descriptions from OWL Ontologies: the NaturalOWL System." Journal of Artificial Intelligence Research 48 (November 22, 2013): 671–715. http://dx.doi.org/10.1613/jair.4017.

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We present NaturalOWL, a natural language generation system that produces texts describing individuals or classes of OWL ontologies. Unlike simpler OWL verbalizers, which typically express a single axiom at a time in controlled, often not entirely fluent natural language primarily for the benefit of domain experts, we aim to generate fluent and coherent multi-sentence texts for end-users. With a system like NaturalOWL, one can publish information in OWL on the Web, along with automatically produced corresponding texts in multiple languages, making the information accessible not only to computer programs and domain experts, but also end-users. We discuss the processing stages of NaturalOWL, the optional domain-dependent linguistic resources that the system can use at each stage, and why they are useful. We also present trials showing that when the domain-dependent llinguistic resources are available, NaturalOWL produces significantly better texts compared to a simpler verbalizer, and that the resources can be created with relatively light effort.

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Medina-Moreira, José, Katty Lagos-Ortiz, Harry Luna-Aveiga, Oscar Apolinario-Arzube, María del Pilar Salas-Zárate, and Rafael Valencia-García. "Knowledge Acquisition Through Ontologies from Medical Natural Language Texts." Journal of Information Technology Research 10, no. 4 (2017): 56–69. http://dx.doi.org/10.4018/jitr.2017100104.

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Ontologies are used to represent knowledge and they have become very important in the Semantic Web era. Ontologies evolve continuously during their life cycle to adapt to new requirements and needs, especially in the biomedical field, where the number of ontologies and their complexity have increased during the last years. On the other hand, a vast amount of clinical knowledge resides in natural language texts. For these reasons, building and maintaining biomedical ontologies from natural language texts is a relevant and challenging issue. In order to provide a general solution and to minimize the experts' participation during the ontology enriching process, a methodology for extracting terms and relations from natural language texts is proposed in this work. This framework is based on linguistic and statistical methods and semantic role labeling technologies, having been validated in the domain of diabetes, where they have obtained encouraging results with an F-measure of 82.1% and 79.9% for concepts and relations, respectively.

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Atkinson, John, Anita Ferreira, and Elvis Aravena. "Discovering implicit intention-level knowledge from natural-language texts." Knowledge-Based Systems 22, no. 7 (2009): 502–8. http://dx.doi.org/10.1016/j.knosys.2008.10.007.

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Gupta, Ashutosh, and Suneeta Agarwal. "A fast dynamic compression scheme for natural language texts." Computers & Mathematics with Applications 60, no. 12 (2010): 3139–51. http://dx.doi.org/10.1016/j.camwa.2010.10.019.

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Nursakitov, K., A. Bekishev, S. Kumargazhanova, and A. Urkumbaeva. "REVIEW OF METHODS FOR DETERMINING THE TONATION OF TEXTS IN NATURAL LANGUAGES." Bulletin of Shakarim University. Technical Sciences, no. 1(9) (March 31, 2023): 59–67. http://dx.doi.org/10.53360/2788-7995-2023-1(9)-8.

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The analysis of sentiment in user comments finds application in many areas, such as evaluating the quality of goods and services, analyzing emotions in messages, and detecting phishing advertisements. There are numerous methods for analyzing the sentiment of textual data in the Russian language, but automatic sentiment analysis of Russian-language texts is much less developed than for other major world languages. This article is part of a broader study on the creation of an information system for detecting dangerous content in the cyberspace of Kazakhstan. The purpose of this article is to provide an analytical review of the different approaches to sentiment analysis of Russian-language texts and to compare modern methods for solving the problem of text classification. Additionally, the article seeks to identify development trends in this area and select the best algorithms for use in further research. The review covers different methods for text data preprocessing, vectorization, and machine classification for sentiment analysis of texts, and it concludes with an analysis of existing databases on this topic. The article identifies some of the main unresolved problems in sentiment analysis of Russianlanguage texts and discusses planned further research.

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Henrik, Leopold, Mendling Jan, and Polyvyanyy Artem. "Supporting Process Model Validation through Natural Language Generation." IEEE Transactions on Software Engineering 40, no. 8 (2014): 818–40. https://doi.org/10.1109/TSE.2014.2327044.

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The design and development of process-aware information systems is often supported by specifying requirements as business process models. Although this approach is generally accepted as an effective strategy, it remains a fundamental challenge to adequately validate these models given the diverging skill set of domain experts and system analysts. As domain experts often do not feel confident in judging the correctness and completeness of process models that system analysts create, the validation often has to regress to a discourse using natural language. In order to support such a discourse appropriately, so-called verbalization techniques have been defined for different types of conceptual models. However, there is currently no sophisticated technique available that is capable of generating natural-looking text from process models. In this paper, we address this research gap and propose a technique for generating natural language texts from business process models. A comparison with manually created process descriptions demonstrates that the generated texts are superior in terms of completeness, structure, and linguistic complexity. An evaluation with users further demonstrates that the texts are very understandable and effectively allow the reader to infer the process model semantics. Hence, the generated texts represent a useful input for process model validation.

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Solnyshkina, Marina Ivanovna, Danielle S. McNamara, and Radif Rifkatovich Zamaletdinov. "Natural language processing and discourse complexity studies." Russian Journal of Linguistics 26, no. 2 (2022): 317–41. http://dx.doi.org/10.22363/2687-0088-30171.

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The study presents an overview of discursive complexology, an integral paradigm of linguistics, cognitive studies and computer linguistics aimed at defining discourse complexity. The article comprises three main parts, which successively outline views on the category of linguistic complexity, history of discursive complexology and modern methods of text complexity assessment. Distinguishing the concepts of linguistic complexity, text and discourse complexity, we recognize an absolute nature of text complexity assessment and relative nature of discourse complexity, determined by linguistic and cognitive abilities of a recipient. Founded in the 19th century, text complexity theory is still focused on defining and validating complexity predictors and criteria for text perception difficulty. We briefly characterize the five previous stages of discursive complexology: formative, classical, period of closed tests, constructive-cognitive and period of natural language processing. We also present the theoretical foundations of Coh-Metrix, an automatic analyzer, based on a five-level cognitive model of perception. Computing not only lexical and syntactic parameters, but also text level parameters, situational models and rhetorical structures, Coh-Metrix provides a high level of accuracy of discourse complexity assessment. We also show the benefits of natural language processing models and a wide range of application areas of text profilers and digital platforms such as LEXILE and ReaderBench. We view parametrization and development of complexity matrix of texts of various genres as the nearest prospect for the development of discursive complexology which may enable a higher accuracy of inter- and intra-linguistic contrastive studies, as well as automating selection and modification of texts for various pragmatic purposes.

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Zagorulko, Yu A. "Development of a method for automatic extraction of ontology entity names from natural language texts." Mechanics and Technologies, no. 3 (September 30, 2024): 486–94. http://dx.doi.org/10.55956/ziyq7931.

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Domain ontologies play a crucial role in organizing, sharing, and reusing domain-specific knowledge within software systems. However, developing a software ontology is a time-consuming and complex process. To create such an ontology, a large number of relevant publications must be analyzed. This task of enriching the ontology with data from these sources can be streamlined and accelerated through the use of lexical and syntactic patterns derived from ontological design. This paper presents a method for the automated construction of ontologies within a scientific domain, leveraging a system of heterogeneous ontological design patterns (ODPs). This system includes ODPs tailored for ontology developers and automatically generated lexical and syntactic patterns, which can be used to enrich ontologies with information extracted from natural language texts

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Cohen, A., R. N. Mantegna, and S. Havlin. "Numerical Analysis of Word Frequencies in Artificial and Natural Language Texts." Fractals 05, no. 01 (1997): 95–104. http://dx.doi.org/10.1142/s0218348x97000103.

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We perform a numerical study of the statistical properties of natural texts written in English and of two types of artificial texts. As statistical tools we use the conventional Zipf analysis of the distribution of words and the inverse Zipf analysis of the distribution of frequencies of words, the analysis of vocabulary growth, the Shannon entropy and a quantity which is a nonlinear function of frequencies of words, the frequency "entropy". Our numerical results, obtained by investigation of eight complete books and sixteen related artificial texts, suggest that, among these analyses, the analysis of vocabulary growth shows the most striking difference between natural and artificial texts. Our results also suggest that, among these analyses, those who give a greater weight to low frequency words succeed better in distinguishing between natural and artificial texts. The inverse Zipf analysis seems to succeed better than the conventional Zipf analysis and the frequency "entropy" better than the usual word entropy. By studying the scaling behavior of both entropies as a function of the total number of words T of the investigated text, we find that the word relative entropy scales with the same functional form for both natural and artificial texts but with a different parameter, while the frequency relative "entropy" decreases monotonically with T for the artificial texts while having a minimum at T≈104 for the natural texts.

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Aqqad, Muslihul, and Nova Rijati. "Classification of Banjarese Hulu and Kuala Dialects in Banjarese Prose Texts." Eduvest - Journal of Universal Studies 4, no. 10 (2024): 9676–87. http://dx.doi.org/10.59188/eduvest.v4i10.39017.

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This research focuses on classifying the Hulu and Kuala Banjarese dialects in the prose text “Datu Kandangan and Datu Kartamina”. These dialects represent linguistic variations resulting from geographical, social, and cultural differences among language communities, particularly in South Kalimantan, Indonesia. Language analysis methods such as Python Natural Language Toolkit (NLTK), NumPy, and Latent Dirichlet Allocation (LDA) Visualization (LyLDAvis) were employed to classify the dialects, involving data preprocessing steps like tokenization, punctuation removal, stop word normalization, and stemming. The research findings reveal the superiority of the "Naive Bayes" method over the "Boolean Query," achieving high accuracy in identifying positive examples and classifying texts into Upper and Lower Banjar dialects. The "Naive Bayes" method outperforms the "Boolean Query" with precision and recall values of 0.955563 and 0.956098, while the "Boolean Query" only reaches 0.021416 and 0.146341. This study makes a significant scholarly contribution to understanding language and cultural diversity in South Kalimantan, opening opportunities for further exploration in developing Natural Language Processing (NLP) technology for Indonesian regional languages.

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Ling, Xiang, Lingfei Wu, Saizhuo Wang, et al. "Deep Graph Matching and Searching for Semantic Code Retrieval." ACM Transactions on Knowledge Discovery from Data 15, no. 5 (2021): 1–21. http://dx.doi.org/10.1145/3447571.

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Code retrieval is to find the code snippet from a large corpus of source code repositories that highly matches the query of natural language description. Recent work mainly uses natural language processing techniques to process both query texts (i.e., human natural language) and code snippets (i.e., machine programming language), however, neglecting the deep structured features of query texts and source codes, both of which contain rich semantic information. In this article, we propose an end-to-end deep graph matching and searching (DGMS) model based on graph neural networks for the task of semantic code retrieval. To this end, we first represent both natural language query texts and programming language code snippets with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet. In particular, DGMS not only captures more structural information for individual query texts or code snippets, but also learns the fine-grained similarity between them by cross-attention based semantic matching operations. We evaluate the proposed DGMS model on two public code retrieval datasets with two representative programming languages (i.e., Java and Python). Experiment results demonstrate that DGMS significantly outperforms state-of-the-art baseline models by a large margin on both datasets. Moreover, our extensive ablation studies systematically investigate and illustrate the impact of each part of DGMS.

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Zhang, Beibei, Hao Yan, Jiawei Wu, and Ping Qu. "Application of Semantic Analysis Technology in Natural Language Processing." Journal of Computer Technology and Applied Mathematics 1, no. 2 (2024): 27–34. https://doi.org/10.5281/zenodo.12601286.

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With the evolution of intelligent technology, especially in the field of natural language processing, semantic analysis has become a powerful tool for studying users. It is good at processing and interpreting numerous unformatted information texts produced in user interactions. This article takes an in-depth look at how to improve the user experience design process using intelligent semantic analysis technology, which can quickly and accurately extract important information from user feedback through automated analysis. Although some current semantic analysis technologies face accuracy tests when analyzing data rich in contextual information, by integrating algorithms such as TF-IDF and Word2vec with deep learning models, the accuracy and efficiency of analysis and interpretation can be achieved significantly improved. In particular, this study developed a semantic clustering analysis technology for short texts, confirming its significant effect in classifying user feedback and assisting product design decision-making. In the future, with technological advancement, artificial intelligence is expected to be routinely used in consumer research, thereby making product design closer to user needs.

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Mukanova, Assel, Marek Milosz, Assem Dauletkaliyeva, et al. "LLM-Powered Natural Language Text Processing for Ontology Enrichment." Applied Sciences 14, no. 13 (2024): 5860. http://dx.doi.org/10.3390/app14135860.

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This paper describes a method and technology for processing natural language texts and extracting data from the text that correspond to the semantics of an ontological model. The proposed method is distinguished by the use of a Large Language Model algorithm for text analysis. The extracted data are stored in an intermediate format, after which individuals and properties that reflect the specified semantics are programmatically created in the ontology. The proposed technology is implemented using the example of an ontological model that describes the geographical configuration and administrative–territorial division of Kazakhstan. The proposed method and technology can be applied in any subject areas for which ontological models have been developed. The results of the study can significantly improve the efficiency of using knowledge bases based on semantic networks by converting texts in natural languages into semantically linked data.

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Kochkonbaeva, B., and A. Aldosova. "Automatic processing of text in natural language." Bulletin of Science and Practice 4, no. 7 (2018): 216–21. https://doi.org/10.5281/zenodo.1312217.

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In this article, questions of artificial intelligence, in particular, automatic processing in natural language texts are considered. As well as types of wordform analysis are considered and an algorithm for finding the initial form of the word is proposed.

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Sargsyan, Inna Robertovna. "PRECEDENT TEXTS FROM THE PERSPECTIVE OF AUTHENTICITY IN THE RCT TRAINING SYSTEM." Annali d'Italia 52 (February 25, 2024): 67–70. https://doi.org/10.5281/zenodo.10703005.

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Having analyzed various points of view regarding the use of authentic texts in the process of teaching foreign languages, it can be argued that these materials have natural lexical content and grammatical forms, they are characterized by great situational adequacy and socio-cultural saturation, therefore they are able to reflect the cultural characteristics of the country of the language being studied. Due to these features, authentic texts can and should be successfully used in the process of teaching foreign languages, including RFL. Howeve r, they must certainly be pre-prepared for students' perception with the help of specially designed exercises, tasks and situations that are as close as possible to the natural situations of communication with native speakers of the title language. At the same time, of course, the structural, lexical and functional authenticity of educational materials (texts) is preserved.

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Korycinski, C., and Alan F. Newell. "Natural-language processing and automatic indexing." Indexer: The International Journal of Indexing: Volume 17, Issue 1 17, no. 1 (1990): 21–29. http://dx.doi.org/10.3828/indexer.1990.17.1.8.

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The task of producing satisfactory indexes by automatic means has been tackled on two fronts: by statistical analysis of text and by attempting content analysis of the text in much the same way as a human indexcr does. Though statistical techniques have a lot to offer for free-text database systems, neither method has had much success with back-of-the-bopk indexing. This review examines some problems associated with the application of natural-language processing techniques to book texts.

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Furlan, Bojan, Vuk Batanović, and Boško Nikolić. "Semantic similarity of short texts in languages with a deficient natural language processing support." Decision Support Systems 55, no. 3 (2013): 710–19. http://dx.doi.org/10.1016/j.dss.2013.02.002.

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Solovyev, Valery, Vladimir Polyakov, Vladimir Ivanov, Ivan Anisimov, and Andrey Ponomarev. "An Approach to Semantic Natural Language Processing of Russian Texts." Research in Computing Science 65, no. 1 (2013): 65–73. http://dx.doi.org/10.13053/rcs-65-1-5.

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Kryvyi, S. L., N. P. Darchuk, and A. I. Provotar. "Ontological similar systems for analysis of texts of natural language." PROBLEMS IN PROGRAMMING, no. 2-3 (2018): 132–39. http://dx.doi.org/10.15407/pp2018.02.132.

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