Please use this identifier to cite or link to this item: http://repository.kpi.kharkov.ua/handle/KhPI-Press/57935
Title: Building the Semantic Similarity Model for Social Network Data Streams
Authors: Petrasova, S. V.
Khairova, N. F.
Lewoniewski, Wlodzimierz
Keywords: social network; data stream; collocations; semantic similarity; blogs; corpus; Universal Dependencies; WordNet
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers
Citation: Petrasova 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.
Abstract: 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.
DOI: doi.org/10.1109/DSMP.2018.8478480
URI: http://repository.kpi.kharkov.ua/handle/KhPI-Press/57935
Appears in Collections:Кафедра "Інтелектуальні комп'ютерні системи"

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