Semantic similarity identification for short text fragments

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dc.citation.epage59
dc.citation.journalTitleComputational Linguistics and Intelligent Systems
dc.citation.spage57
dc.citation.volume2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019
dc.contributor.affiliationNational Technical University «Kharkiv Polytechnic Institute»
dc.contributor.authorChuiko, Viktoriia
dc.contributor.authorKhairova, Nina
dc.coverage.placenameLviv
dc.date.accessioned2019-10-31T13:21:07Z
dc.date.available2019-10-31T13:21:07Z
dc.date.created2019-04-18
dc.date.issued2019-04-18
dc.description.abstractThe paper contains review of the existing methods for semantic similarity identification, such as methods based on the distance between concepts and methods based on lexical intersection. We proposed a method for measuring the semantic similarity of short text fragment, i.e. two sentences. Also, we created corpus of mass-media text. It contains articles of Kharkiv news, that were sorted by their source and date. Then we annotated texts. We defined semantic similarity of sentences manually. In this way, we created learning corpus for our future system.
dc.format.extent57-59
dc.format.pages3
dc.identifier.citationChuiko V. Semantic similarity identification for short text fragments / Viktoriia Chuiko, Nina Khairova // Computational Linguistics and Intelligent Systems. — Lviv : Lviv Politechnic Publishing House, 2019. — Vol 2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019. — P. 57–59. — (Student section).
dc.identifier.citationenChuiko V. Semantic similarity identification for short text fragments / Viktoriia Chuiko, Nina Khairova // Computational Linguistics and Intelligent Systems. — Lviv Politechnic Publishing House, 2019. — Vol 2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019. — P. 57–59. — (Student section).
dc.identifier.issn2523-4013
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/45497
dc.language.isoen
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofComputational Linguistics and Intelligent Systems (2), 2019
dc.relation.referencesen1. Anisimov, A., Glybovets,M., Marchenki,O., Kysenko, V.: The method for calculating of semantic closeness of natural language words meanings (2011)
dc.relation.referencesen2. Islam, A., Inkpen, D.: Semantic Similarity of Short Texts. University of information technology & sciences (2009)
dc.relation.referencesen3. Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: International Joint Conference for Artifcial Intelligence (IJCAI-95),pp. 448-453(1995)
dc.relation.referencesen4. Leacock, C., Chodorow, M., Miller, G. A.: Using corpus statistics and wordnet relations for sense identifcation. Computational Linguistics, 24(1),pp. 147-165(1998)
dc.relation.referencesen5. Lesk, M.: Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In:SIGDOC’86: Proceedings of the 5th annual international conference on Systems documentation, pp. 24-26. New York, NY, USA. ACM(1986)
dc.relation.referencesen6. Microsoft Research Paraphrase Corpus Homepage,https://www.microsoft.com/en-us/download/details.aspx?id=52398, last accessed 2019/03/01.
dc.relation.urihttps://www.microsoft.com/en-us/download/details.aspx?id=52398
dc.rights.holder© 2019 for the individual papers by the papers’ authors. Copying permitted only for private and academic purposes. This volume is published and copyrighted by its editors.
dc.subjectsemantic similarity
dc.subjectshort text fragments
dc.subjectcorpus of mass-media text
dc.subjectautomatic identification
dc.titleSemantic similarity identification for short text fragments
dc.typeArticle

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