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Постійне посилання на розділhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/35393
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Документ The Logical-Linguistic Model of Fact Extraction from English Texts(2016) Khairova, N. F.; Petrasova, S. V.; Gautam, Ajit Pratap SinghIn this paper we suggest the logical-linguistic model that allows extracting required facts from English sentences. We consider the fact in the form of a triplet: Subject > Predicate > Object with the Predicate representing relations and the Object and Subject pointing out two entities. The logical-linguistic model is based on the use of the grammatical and semantic features of words in English sentences. Basic mathematical characteristic of our model is logical-algebraic equations of the finite predicates algebra. The model was successfully implemented in the system that extracts and identifies some facts from Web-content of a semi-structured and non-structured English text.Документ Logical-linguistic model for multilingual Open Information Extraction(2020) Khairova, N. F.; Mamyrbayev, Orken; Mukhsina, Kuralay; Kolesnyk, AnastasiiaOpen Information Extraction (OIE) is a modern strategy to extract the triplet of facts from Web-document collections. However, most part of the current OIE approaches is based on NLP techniques such as POS tagging and dependency parsing, which tools are accessible not to all languages. In this paper, we suggest the logical-linguistic model, which basic mathematical means are logical-algebraic equations of finite predicates algebra. These equations allow expressing a semantic role of the participant of a triplet of the fact (Subject-Predicate-Object) due to the relations of grammatical characteristics of words in the sentence. We propose the model that extracts the unlimited domain-independent number of facts from sentences of different languages. The use of our model allows extracting the facts from unstructured texts without requiring a pre-specified vocabulary, by identifying relations in phrases and associated arguments in arbitrary sentences of English, Kazakh, and Russian languages. We evaluate our approach on corpora of three languages based on English and Kazakh bilingual news websites. We achieve the precision of facts extraction over 87% for English corpus, over 82% for Russian corpus and 71% for Kazakh corpus.