Building the Semantic Similarity Model for Social Network Data Streams

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dc.contributor.authorPetrasova, S. V.en
dc.contributor.authorKhairova, N. F.en
dc.contributor.authorLewoniewski, Wlodzimierzen
dc.date.accessioned2022-09-10T18:03:50Z
dc.date.available2022-09-10T18:03:50Z
dc.date.issued2018
dc.description.abstractThis 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.en
dc.identifier.citationPetrasova S. Building the Semantic Similarity Model for Social Network Data Streams / S. Petrasova, N. Khairova, W. Lewoniewski // 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), 21-25 August 2018, Lviv, Ukraine : bk. of abstr. – Lviv : IEEE, 2018. – P. 21-24.en
dc.identifier.doidoi.org/10.1109/DSMP.2018.8478480
dc.identifier.urihttps://repository.kpi.kharkov.ua/handle/KhPI-Press/57935
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineersen
dc.subjectsocial networken
dc.subjectdata streamen
dc.subjectcollocationsen
dc.subjectsemantic similarityen
dc.subjectblogsen
dc.subjectcorpusen
dc.subjectUniversal Dependenciesen
dc.subjectWordNeten
dc.titleBuilding the Semantic Similarity Model for Social Network Data Streamsen
dc.typeThesisen

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