Semantic similarity identification for short text fragments
dc.citation.epage | 59 | |
dc.citation.journalTitle | Computational Linguistics and Intelligent Systems | |
dc.citation.spage | 57 | |
dc.citation.volume | 2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019 | |
dc.contributor.affiliation | National Technical University «Kharkiv Polytechnic Institute» | |
dc.contributor.author | Chuiko, Viktoriia | |
dc.contributor.author | Khairova, Nina | |
dc.coverage.placename | Lviv | |
dc.date.accessioned | 2019-10-31T13:21:07Z | |
dc.date.available | 2019-10-31T13:21:07Z | |
dc.date.created | 2019-04-18 | |
dc.date.issued | 2019-04-18 | |
dc.description.abstract | The 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.extent | 57-59 | |
dc.format.pages | 3 | |
dc.identifier.citation | Chuiko 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.citationen | Chuiko 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.issn | 2523-4013 | |
dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/45497 | |
dc.language.iso | en | |
dc.publisher | Lviv Politechnic Publishing House | |
dc.relation.ispartof | Computational Linguistics and Intelligent Systems (2), 2019 | |
dc.relation.referencesen | 1. Anisimov, A., Glybovets,M., Marchenki,O., Kysenko, V.: The method for calculating of semantic closeness of natural language words meanings (2011) | |
dc.relation.referencesen | 2. Islam, A., Inkpen, D.: Semantic Similarity of Short Texts. University of information technology & sciences (2009) | |
dc.relation.referencesen | 3. 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.referencesen | 4. 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.referencesen | 5. 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.referencesen | 6. Microsoft Research Paraphrase Corpus Homepage,https://www.microsoft.com/en-us/download/details.aspx?id=52398, last accessed 2019/03/01. | |
dc.relation.uri | https://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.subject | semantic similarity | |
dc.subject | short text fragments | |
dc.subject | corpus of mass-media text | |
dc.subject | automatic identification | |
dc.title | Semantic similarity identification for short text fragments | |
dc.type | Article |
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