ParaPhraser: Russian paraphrase corpus and shared task

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dc.contributor.author Pivovarova, Lidia
dc.contributor.author Pronoza, Ekaterina
dc.contributor.author Yagunova, Elena
dc.contributor.author Pronoza, Anton
dc.contributor.editor Filchenkov, Andrey
dc.contributor.editor Pivovarova, Lidia
dc.contributor.editor Žižka, Jan
dc.date.accessioned 2018-02-12T13:04:02Z
dc.date.available 2018-02-12T13:04:02Z
dc.date.issued 2017
dc.identifier.citation Pivovarova , L , Pronoza , E , Yagunova , E & Pronoza , A 2017 , ParaPhraser: Russian paraphrase corpus and shared task . in A Filchenkov , L Pivovarova & J Žižka (eds) , Artificial Intelligence and Natural Language : 6th Conference, AINL 2017, St. Petersburg, Russia, September 20–23, 2017, Revised Selected Papers . Communications in Computer and Information Science , vol. 789 , Springer , Cham , pp. 211-225 , Conference on Artificial Intelligence and Natural Language , St.Petersburg , Russian Federation , 20/09/2017 . https://doi.org/10.1007/978-3-319-71746-3_18
dc.identifier.citation conference
dc.identifier.other PURE: 94679066
dc.identifier.other PURE UUID: 710aa7b2-4f97-41b3-b777-b6ffbf0606cb
dc.identifier.other Scopus: 85037545952
dc.identifier.other WOS: 000437301200018
dc.identifier.other ORCID: /0000-0002-0026-9902/work/81734756
dc.identifier.uri http://hdl.handle.net/10138/232301
dc.description.abstract The paper describes the results of the First Russian Paraphrase Detection Shared Task held in St.-Petersburg, Russia, in October 2016. Research in the area of paraphrase extraction, detection and generation has been successfully developing for a long time while there has been only a recent surge of interest towards the problem in the Russian community of computational linguistics. We try to overcome this gap by introducing the project ParaPhraser.ru dedicated to the collection of Russian paraphrase corpus and organizing a Paraphrase Detection Shared Task, which uses the corpus as the training data. The participants of the task applied a wide variety of techniques to the problem of paraphrase detection, from rule-based approaches to deep learning, and results of the task reflect the following tendencies: the best scores are obtained by the strategy of using traditional classifiers combined with fine-grained linguistic features, however, complex neural networks, shallow methods and purely technical methods also demonstrate competitive results. en
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartof Artificial Intelligence and Natural Language
dc.relation.ispartofseries Communications in Computer and Information Science
dc.relation.isversionof 978-3-319-71745-6
dc.relation.isversionof 978-3-319-71746-3
dc.rights.uri info:eu-repo/semantics/closedAccess
dc.subject 6121 Languages
dc.subject 113 Computer and information sciences
dc.title ParaPhraser: Russian paraphrase corpus and shared task en
dc.type Conference contribution
dc.contributor.organization Department of Computer Science
dc.contributor.organization Computational Linguistics research group / Roman Yangarber
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1007/978-3-319-71746-3_18
dc.relation.issn 1865-0929
dc.rights.accesslevel closedAccess

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