Browsing by Author "Kuropiatnyk, Olena S."

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    Automation of Template Formation to Identify the Structure of Natural Language Documents
    (CEUR-WS Team, Aachen, Germany, 2021) Kuropiatnyk, Olena S.; Shynkarenko, Viktor I.
    ENG: In the task of text borrowings and plagiarism detection, it is important to take into account the structure of the document. This allows getting a more accurate assessment of the text and reducing the volume of material for comparison. Using a template allows identifying the structure of the document. The paper presents a constructive synthesizing model for automating the construction of a structural template of a document. Possible implementations of some algorithms by means of programming in C# are considered. Their comparative assessment is performed. Possible modification of the template is presented to increase the importance of keywords and simplify the xml-tree, which is a template.
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    Constructive Model of the Natural Language
    (Institute of Informatics, University of Szeged, Hungary, 2018) Shynkarenko, Viktor I.; Kuropiatnyk, Olena S.
    ENG: The paper deals with the natural lenguage model. Elements of the model (the language constructions) are images with such attributes as sounds, let- ters, morphemes, words and other lexical and syntactic components of the language. Based on the analysis of processes of the world perception, visual and associative thinking, the operations of formation and transformation of images are pointed out. The model can be applied in the semantic NLP.
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    A Dual Approach to Establishing the Authority of Technical Natural Language Texts and Their Components
    ( Ukrainian State University of Science and Technologies, Dnipro, 2023) Shynkarenko, Viktor I.; Demidovich, Inna; Kuropiatnyk, Olena S.
    ENG: Purpose. The study is aimed at testing the hypothesis that it is possible to determine plagiarism by methods of establishing the authorship of a text without using a text bank and their direct comparison. Methodology. Construc-tive and productive models of the processes of establishing the authorship of technical texts for two methods have been developed. The first method is based on the formation of a text model in the form of a set of formal substitution rules with probabilistic weights (as in stochastic formal grammars), which reflects the syntactic features and patterns of text formation by the author. The degree of similarity between the text under study and another text is determined by comparing their models. The second method is a classical approach to detecting borrowings (plagiarism) by directly comparing the text under study with an existing text bank, highlighting repeated text fragments, and determining the degree of originality. Experiments were conducted to establish the correlation between the results of these two methods. The experimental base consisted of 509 text sections of theses of students majoring in «Software Engineering». Findings. Experimental studies have made it possible to establish a high correlation between the results of the two methods. Correlation coefficients in the range of 0.75...1.0 and with an average value of 0.88 were obtained provided that borrowings are taken into account for text fragments of at least five words in length. Originality. For the first time, the authors have identified the possibilities and proposed methods for indirect plagiarism detection without using a large text bank. The essence of the model is to formalize the representation of the author's sentence syntax by a set of substitution rules with probabilistic weights. Practical value. Based on the results obtained, the possibilities for detecting borrowings have been expanded and the effectiveness of the corre-sponding methods has been increased. Recommendations on the parameters of classical methods for detecting borrowings have been obtained, in particular, it is recommended to take into account text fragments of at least five words in length as a rational parameter when using borrowing detection systems. The possibilities of text authorship detection methods tested on fiction texts are extended to technical texts.
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    Geometric Fractals' Constructive-Synthesizing Models using Ontological Means
    (CEUR-WS Team, Aachen, Germany, 2024) Kuropiatnyk, Olena S.; Shynkarenko, Viktor I.; Zhuchyi, Larysa I.; Lyakhova, Maria
    ENG: The paradigm of constructive-synthesizing modelling is based on the idea of the world as a collection of different structures. The development of constructive-synthesizing modelling provides an opportunity to automate the formation of structures. Automation possibilities depend on the degree of formalization and the quality of the corresponding models. In this work, the formalization of constructive-synthesizing models is enriched by the ontological representation of knowledge. This approach is demonstrated in the formation and display of geometric fractals. The developed models are implemented by software tools using Java and Apache Jena framework. It is possible to change the basic elements of fractals based on their ontological representation.
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    Text Borrowings Detection System for Natural Language Structured Digital Documents
    (CEUR-WS Team, Aachen, Germany, 2020) Kuropiatnyk, Olena S.; Shynkarenko, Viktor I.
    ENG: Interpretation of results is an important stage in text borrowings detection systems. Necessary to take into consideration the tree structure of the document and the general content of structural elements (sections) is the reason for that. In article method comparison of structured document is developed. Formalization of comparison document process is based on constructive-synthesizing modeling. Document structure is processed using templates. They contain information about section and subsections titles and keywords sets. The base of natural language text comparison is text graph representation model. It represents a text as graphs set for improving borrowings retrieval in texts of database. On base of these models and method text borrowings detection system is developed for comparison digital structured natural language documents. The paper presents the features of the system and its advantages. System architecture is described and its time efficiency investigated.