Neural architectures for named entity recognition

Lample, Guillaume; Ballesteros, Miguel; Subramanian, Sandeep; Kawakami, Kazuya; Dyer, Chris

Neural architectures for named entity recognition

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dc.contributor.author Lample, Guillaumeca
dc.contributor.author Ballesteros, Miguelca
dc.contributor.author Subramanian, Sandeepca
dc.contributor.author Kawakami, Kazuyaca
dc.contributor.author Dyer, Chrisca
dc.date.accessioned 2016-12-12T10:24:16Z
dc.date.available 2016-12-12T10:24:16Z
dc.date.issued 2016ca
dc.description Comunicació presentada a la 2016 Conference of the North American Chapter of the Association for Computational Linguistics, celebrada a San Diego (CA, EUA) els dies 12 a 17 de juny 2016.ca
dc.description.abstract State-of-the-art named entity recognition systems/nrely heavily on hand-crafted features and/ndomain-specific knowledge in order to learn/neffectively from the small, supervised training/ncorpora that are available. In this paper, we/nintroduce two new neural architectures—one/nbased on bidirectional LSTMs and conditional/nrandom fields, and the other that constructs/nand labels segments using a transition-based/napproach inspired by shift-reduce parsers./nOur models rely on two sources of information/nabout words: character-based word/nrepresentations learned from the supervised/ncorpus and unsupervised word representations/nlearned from unannotated corpora. Our/nmodels obtain state-of-the-art performance in/nNER in four languages without resorting to/nany language-specific knowledge or resources/nsuch as gazetteers.en
dc.description.sponsorship This work was sponsored in part by the Defense/nAdvanced Research Projects Agency (DARPA)/nInformation Innovation Office (I2O) under the/nLow Resource Languages for Emergent Incidents/n(LORELEI) program issued by DARPA/I2O under/nContract No. HR0011-15-C-0114. Miguel Ballesteros/nis supported by the European Commission under/nthe contract numbers FP7-ICT-610411 (project/nMULTISENSOR) and H2020-RIA-645012 (project/nKRISTINA).en
dc.format.mimetype application/pdfca
dc.identifier.citation Lample G, Ballesteros M, Subramanian S, Kawakami K, Dyer C. Neural architectures for named entity recognition. In: Knight K, Lopez A, Mitchell M, editors. Human Language Technologies. 2016 Conference of the North American Chapter of the Association for Computational Linguistics; 2016 June 12-17; San Diego (CA, USA). [S.l.]: Association for Computational Linguistics (ACL); 2016. p. 260-270.ca
dc.identifier.uri http://hdl.handle.net/10230/27725
dc.language.iso engca
dc.publisher ACL (Association for Computational Linguistics)ca
dc.relation.ispartof Knight K, Lopez A, Mitchell M, editors. Human Language Technologies. 2016 Conference of the North American Chapter of the Association for Computational Linguistics; 2016 June 12-17; San Diego (CA, USA). [S.l.]: Association for Computational Linguistics (ACL); 2016. p. 260-270.
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/645012ca
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/610411
dc.rights.accessRights info:eu-repo/semantics/openAccessca
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/
dc.subject.other Tractament del llenguatge natural (Informàtica)ca
dc.subject.other Lingüística computacionalca
dc.title Neural architectures for named entity recognitionca
dc.type info:eu-repo/semantics/conferenceObjectca
dc.type.version info:eu-repo/semantics/publishedVersionca

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