Building the Semantic Similarity Model for Social Network Data Streams
Svitlana Petrasova , Nina Khairova , Włodzimierz Lewoniewski
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.
|Publication size in sheets||0.5|
|Book||Vynokurova Olena, Peleshko Dmytro (eds.): Proceedings of the 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), 2018, Institute of Electrical and Electronics Engineers, ISBN 978-1-5386-2874-4, 598 p.|
|Keywords in English||social network; data stream; collocations; semantic similarity; blogs; corpus; Universal Dependencies; WordNet|
|Score||= 20.0, 11-09-2020, ChapterFromConference|
|Publication indicators||= 1|
|Citation count*||5 (2020-09-28)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.