Кафедра "Інтелектуальні комп'ютерні системи"

Постійне посилання колекціїhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/2423

Офіційний сайт кафедри http://web.kpi.kharkov.ua/iks

Кафедра "Інтелектуальні комп’ютерні системи" заснована 12 лютого 2007 року на базі спеціальності "Прикладна лінгвістика".

У 2009 році на базі кафедри спільно з Українським мовно-інформаційним фондом НАН України було створено Науково-дослідний центр інтелектуальних систем і комп’ютерної лінгвістики.

Кафедра входить до складу Навчально-наукового інституту соціально-гуманітарних технологій Національного технічного університету "Харківський політехнічний інститут".

У складі науково-педагогічного колективу кафедри працюють: 2 доктора технічних наук, 5 кандидатів філологічних наук, 4 кандидата технічних наук, 1 кандидат філософських наук; 2 співробітника мають звання професора, 3 – доцента.

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Using a Technology for Identification of Semantically Connected Text Elements to Determine a Common Information Space
(Springer, 2017) Petrasova, S. V.; Khairova, N. F.
A technology is proposed that makes it possible to determine the common information space of actors of social networks by identifying the semantic equivalence of collocations in texts. The technology includes a model of formal description of semantic and grammatical characteristics of collocates, identification of collocations, and determination of a semantic equivalence predicate of two-word collocations.
Building the Semantic Similarity Model for Social Network Data Streams
(Institute of Electrical and Electronics Engineers, 2018) Petrasova, S. V.; Khairova, N. F.; Lewoniewski, Wlodzimierz
This paper proposes the model for searching similar collocations in English texts in order to determine semantically connected text fragments for social network data streams analysis. The logical-linguistic model uses semantic and grammatical features of words to obtain a sequence of semantically related to each other text fragments from different actors of a social network. In order to implement the model, we leverage Universal Dependencies parser and Natural Language Toolkit with the lexical database WordNet. Based on the Blog Authorship Corpus, the experiment achieves over 0.92 precision.
Automatic Identification of Collocation Similarity
(Institute of Electrical and Electronics Engineers, 2015) Petrasova, S. V.; Khairova, N. F.
This paper proposes a logical and linguistic model for automatic identification of collocation similarity. The method of component analysis is proposed to determine the semantic equivalence between collocates. The set of semantic and grammatical characteristics of collocates is identified by means of algebra of predicates to formalize collocation similarity.
The Logical-Linguistic Model of Fact Extraction from English Texts
(2016) Khairova, N. F.; Petrasova, S. V.; Gautam, Ajit Pratap Singh
In 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.
Open Information Extraction as Additional Source for Kazakh Ontology Generation
(2020) Khairova, N. F.; Petrasova, S. V.; Mamyrbayev, Orken; Mukhsina, Kuralay
Nowadays, structured information that obtains from unstructured texts and Web context can be applied as an additional source of knowledge to create ontologies. In order to extract information from a text and represent it in the RDF-triplets format, we suggest using the Open Information Extraction model. Then we consider the adaptation of the model to fact extraction from unstructured texts in the Kazakh language. In our approach, we identify lexical units that name the participants of the action (the Subject and Object) and semantic relations between them based on words characteristics in a sentence. The model provides semantic functions of the action participants via logical-linguistic equations that express the relations of the grammatical and semantic characteristics of the words in a Kazakh sentence. Using the tag names and some syntactic characteristics of words in the Kazakh sentences as the values of the predicate variables in corresponding equations allows us to extract Subjects, Objects and Predicates of facts from texts of Web content. The experimental research dataset includes texts extracted from Kazakh bilingual news websites. The experiment shows that we can achieve the precision of facts extraction over 71% for Kazakh corpus.
Detecting Collocations Similarity via Logical-Linguistic Model
(Association for Computational Linguistics, USA, 2019) Khairova, N. F.; Petrasova, S. V.; Mamyrbayev, Orken; Mukhsina, Kuralay
Semantic similarity between collocations, along with words similarity, is one of the main issues of NLP. In particular, it might be addressed to facilitate the automatic thesaurus generation. In the paper, we consider the logical-linguistic model that allows defining the relation of semantic similarity of collocations via the logical-algebraic equations. We provide the model for English, Ukrainian and Russian text corpora. The implementation for each language is slightly different in the equations of the finite predicates algebra and used linguistic resources. As a dataset for our experiment, we use 5801 pairs of sentences of Microsoft Research Paraphrase Corpus for English and more than 1000 texts of scientific papers for Russian and Ukrainian.
Method "Mean – Risk" for Comparing Poly-Interval Objects in Intelligent Systems
(2019) Shepelev, Gennady; Khairova, N. F.; Kochueva, Zoia
Problems of comparing poly-interval alternatives under risk in the framework of intelligent computer systems are considered. The problems are common in economy, engineering and in other domains. "Mean-risk" approach was chosen as a tool for comparing. Method for calculation of both main indicators of the "mean-risk" approach – mean and semideviation – for case of polyinterval alternatives is proposed. Method permits to calculate mentioned indicators for interval alternatives represented as fuzzy objects and as generalized interval estimates.
Use of Linguistic Criteria for Estimating of Wikipedia Articles Quality
(2017) Kolesnyk, Anastasiia; Khairova, N. F.
Methods of comparing interval objects in intelligent computer systems
(2017) Shepelev, Gennady; Khairova, N. F.
Problems of expert knowledge representation by means of generalized interval estimates approach and using methods of comparing interval alternatives in the framework of intelligent computer systems are considered. The problems are common in economy, engineering and in other domains. Necessity of multi criteria approach to comparing problem that is taking into account both preference criteria and risk ones is shown. It is proposed to use a multi-steps approach to decision-making concerning choice of preferable interval alternatives. It is based on consistent using of different comparing methods: new collective risk estimating techniques, mean-risk‖ approach (for interval-probability situations) and Savage method (for full uncertainty situations).
The Influence of Various Text Characteristics on the Readability and Content Informativeness
(2019) Khairova, N. F.; Kolesnyk, Anastasiia; Mamyrbayev, Orken; Mukhsina, Kuralay
Currently, businesses increasingly use various external big data sources for extracting and integrating information into their own enterprise information systems to make correct economic decisions, to understand customer needs, and to predict risks. The necessary condition for obtaining useful knowledge from big data is analysing high-quality data and using quality textual data. In the study, we focus on the influence of readability and some particular features of the texts written for a global audience on the texts quality assessment. In order to estimate the influence of different linguistic and statistical factors on the text readability, we reviewed five different text corpora. Two of them contain texts from Wikipedia, the third one contains texts from Simple Wikipedia and two last corpora include scientific and educational texts. We show linguistic and statistical features of a text that have the greatest influence on the text quality for business corporations. Finally, we propose some directions on the way to automatic predicting the readability of texts in the Web.