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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15847
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dc.contributor.authorCopara Zea, Jenny-
dc.contributor.authorOchoa, Jose-
dc.contributor.authorThorne, Camilo-
dc.contributor.authorGlavas, Goran-
dc.date.accessioned2019-01-29T22:19:53Z-
dc.date.available2019-01-29T22:19:53Z-
dc.date.issued2016-
dc.identifier.isbn9783319479545es_PE
dc.identifier.issn3029743es_PE
dc.identifier.urihttp://repositorio.ucsp.edu.pe/handle/UCSP/15847-
dc.description.abstractUnsupervised features based on word representations such as word embeddings and word collocations have shown to significantly improve supervised NER for English. In this work we investigate whether such unsupervised features can also boost supervised NER in Spanish. To do so, we use word representations and collocations as additional features in a linear chain Conditional Random Field (CRF) classifier. Experimental results (82.44% F-score on the CoNLL-2002 corpus) show that our approach is comparable to some state-of-art Deep Learning approaches for Spanish, in particular when using cross-lingual word representations. © Springer International Publishing AG 2016.es_PE
dc.description.uriTrabajo de investigaciónes_PE
dc.language.isoenges_PE
dc.publisherSpringer Verlages_PE
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84994131732&doi=10.1007%2f978-3-319-47955-2_15&partnerID=40&md5=f2f86b9030d7122aa4d20d5b3f39a658es_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceScopuses_PE
dc.subjectArtificial intelligencees_PE
dc.subjectImage segmentationes_PE
dc.subjectCollocationses_PE
dc.subjectConditional random fieldes_PE
dc.subjectCross-linguales_PE
dc.subjectDeep learninges_PE
dc.subjectNamed entity recognitiones_PE
dc.subjectNER for Spanishes_PE
dc.subjectWord collocationses_PE
dc.subjectWord representationses_PE
dc.subjectRandom processeses_PE
dc.titleConditional Random Fields for Spanish Named Entity Recognition Using Unsupervised Featureses_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.doi10.1007/978-3-319-47955-2_15es_PE
Appears in Collections:Artículos de investigación

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