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dc.contributor.authorOrasan, Constantin
dc.contributor.authorMitkov, Ruslan
dc.contributor.authorRanasinghe, Tharindu
dc.date.accessioned2019-09-09T09:54:13Z
dc.date.available2019-09-09T09:54:13Z
dc.date.issued2019-09-02
dc.identifier.citationOrasan, C., Mitkov, R. and Ranasinghe, T. (2019) Semantic textual similarity with siamese neural networks, RANLP 2019, 2nd-4th September 2019, Varna, Bulgaria.en
dc.identifier.isbn9789544520557
dc.identifier.issn1313-8502
dc.identifier.urihttp://hdl.handle.net/2436/622709
dc.description.abstractCalculating the Semantic Textual Similarity (STS) is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. This paper evaluates Siamese recurrent architectures, a special type of neural networks, which are used here to measure STS. Several variants of the architecture are compared with existing methodsen
dc.formatapplication/PDFen
dc.language.isoenen
dc.publisherRANLPen
dc.relation.urlhttp://lml.bas.bg/ranlp2019/accepted.phpen
dc.subjectdeep learningen
dc.subjectnlpen
dc.titleSemantic textual similarity with siamese neural networksen
dc.typeConference contributionen
dc.date.updated2019-09-02T23:04:18Z
dc.conference.nameRecent Advances in Natural Language Processing (RANLP 2019)
dc.conference.locationVarna, Bulgaria
pubs.finish-date2019-09-04
pubs.start-date2019-09-02
dc.date.accepted2019-07-06
rioxxterms.funderUniversity of Wolverhamptonen
rioxxterms.identifier.projectUOW090919COen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
rioxxterms.licenseref.startdate2019-09-02en
refterms.dateFCD2019-09-09T09:53:17Z
refterms.versionFCDAM
refterms.dateFOA2019-09-09T09:54:13Z


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