Similar Text Fragments Extraction for Identifying Common Wikipedia Communities

Svitlana Petrasova , Nina Khairova , Włodzimierz Lewoniewski , Mamyrbayev Orken , Mukhsina Kuralai


Similar text fragments extraction from weakly formalized data is the task of natural language processing and intelligent data analysis and is used for solving the problem of automatic identification of connected knowledge fields. In order to search such common communities in Wikipedia, we propose to use as an additional stage a logical-algebraic model for similar collocations extraction. With Stanford Part-Of-Speech tagger and Stanford Universal Dependencies parser, we identify the grammatical characteristics of collocation words. With WordNet synsets, we choose their synonyms. Our dataset includes Wikipedia articles from different portals and projects. The experimental results show the frequencies of synonymous text fragments in Wikipedia articles that form common information spaces. The number of highly frequented synonymous collocations can obtain an indication of key common up-to-date Wikipedia communities.
Author Svitlana Petrasova
Svitlana Petrasova,,
, Nina Khairova
Nina Khairova,,
, Włodzimierz Lewoniewski (WIiGE / KIE)
Włodzimierz Lewoniewski,,
- Department of Information Systems
, Mamyrbayev Orken - Institute of Information and Computational Technologies, Kazakhstan
Mamyrbayev Orken,,
, Mukhsina Kuralai - Al-Farabi Kazakh National University, Kazakhstan
Mukhsina Kuralai,,
Journal seriesData, ISSN 2306-5729, (0 pkt, indicated Indexes)
Issue year2018
Publication size in sheets0.5
Keywords in PolishWikipedia, eksploracja danych, jakość informacji, informacja
Keywords in EnglishWikipedia, data mining, information quality, information
Languageen angielski
Score (nominal)15
Score sourcejournalIndex
ScoreMinisterial score = 15.0, 11-09-2020, ArticleFromJournal
Publication indicators WoS Citations = 0
Citation count*2 (2020-09-16)
Additional fields
UwagiSpecial Issue : Data Stream Mining and Processing
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