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dc.contributor.authorBrychcín, Tomáš
dc.contributor.authorHercig, Tomáš
dc.contributor.authorSvoboda, Lukáš
dc.contributor.authorKonkol, Michal
dc.contributor.editorSteinberger, Josef
dc.contributor.editorZíma, Martin
dc.contributor.editorFiala, Dalibor
dc.contributor.editorDostal, Martin
dc.contributor.editorNykl, Michal
dc.date.accessioned2017-10-09T11:00:39Z
dc.date.available2017-10-09T11:00:39Z
dc.date.issued2017
dc.identifier.citationSTEINBERGER, Josef ed.; ZÍMA, Martin ed.; FIALA, Dalibor ed.; DOSTAL, Martin ed.; NYKL, Michal ed. Data a znalosti 2017: sborník konference, Plzeň, Hotel Angelo 5. - 6. října 2017. 1. vyd. Plzeň: Západočeská univerzita v Plzni, 2017, s. 111-115. ISBN 978-80-261-0720-0.cs
dc.identifier.isbn978-80-261-0720-0
dc.identifier.urihttps://www.zcu.cz/export/sites/zcu/pracoviste/vyd/online/DataAZnalosti2017.pdf
dc.identifier.urihttp://hdl.handle.net/11025/26346
dc.format5 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherZápadočeská univerzita v Plznics
dc.rights© Západočeská univerzita v Plznics
dc.subjectSemEvalcs
dc.subjectsémantická textová podobnostcs
dc.subjectaspektová analýza sentimentucs
dc.subjectdistribuční sémantikacs
dc.titleUWB at SemEval 2014 and 2016cs
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedInternational Workshop on Semantic Evaluation (SemEval) is an on-going series of evaluations of NLP (Natural Language Processing) algorithms, organized by Association for Computational Linguistics (ACL), the internation-al scientific society which hold the major NLP conferences. The evaluations are intended to explore different aspects of meaning in a natural language. The re-sults of NLP algorithms are compared with human judgments. The submitted systems from research teams across the world are compared in terms of perfor-mance. Our research team actively participates in the SemEval exercises. This paper summarizes our results in the area of semantic textual similarity and as-pect-based sentiment analysis. In 2014 and 2016 our systems were among the best performing in both mentioned tasks.en
dc.subject.translatedSemEvalen
dc.subject.translatedsemantic textual similarityen
dc.subject.translatedaspect-based sentiment analysisen
dc.subject.translateddistributional semanticsen
dc.type.statusPeer-revieweden
Appears in Collections:Konferenční příspěvky / Conference Papers (KIV)
Konferenční příspěvky / Conference papers (NTIS)
Data a znalosti 2017
Data a znalosti 2017

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