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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15792
Title: Exploring Unsupervised Features in Conditional Random Fields for Spanish Named Entity Recognition
Authors: Copara Zea, Jenny
Ochoa, Jose
Thorne, Camilo
Glavas, Goran
Keywords: Image segmentation;;Intelligent systems;Conditional random field;Embeddings;NER for Spanish;Unsupervised features;Word representations;Random processes
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015146121&doi=10.1109%2fBRACIS.2016.059&partnerID=40&md5=42d837464693582128444a9241343d5f
Abstract: Unsupervised features such as word representations mostly given by word embeddings have been shown significantly improve semi supervised Named Entity Recognition (NER) for English language. In this work we investigate whether unsupervised features can boost (semi) 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 and 65.72% F-score on Ancora 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. © 2016 IEEE.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15792
ISBN: urn:isbn:9781509035663
Appears in Collections:Artículos - Ciencia de la computación

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