ParaPhraser: Russian paraphrase corpus and shared task

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http://hdl.handle.net/10138/232301

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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

Titel: ParaPhraser: Russian paraphrase corpus and shared task
Författare: Pivovarova, Lidia; Pronoza, Ekaterina; Yagunova, Elena; Pronoza, Anton
Medarbetare: Filchenkov, Andrey
Pivovarova, Lidia
Žižka, Jan
Upphovmannens organisation: Department of Computer Science
Computational Linguistics research group / Roman Yangarber
Utgivare: Springer
Datum: 2017
Språk: eng
Tillhör serie: Artificial Intelligence and Natural Language
Tillhör serie: Communications in Computer and Information Science
ISBN: 978-3-319-71745-6
978-3-319-71746-3
ISSN: 1865-0929
DOI: https://doi.org/10.1007/978-3-319-71746-3_18
Permanenta länken (URI): http://hdl.handle.net/10138/232301
Abstrakt: 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.
Subject: 6121 Languages
113 Computer and information sciences
Referentgranskad: Ja
Användningsbegränsning: closedAccess


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