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  1. Home
  2. Browse by Author

Browsing by Author "Ochoa, Jose"

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    Conditional Random Fields for Spanish Named Entity Recognition Using Unsupervised Features
    (Springer Verlag, 2016) Copara Zea, Jenny; Ochoa, Jose; Thorne, Camilo; Glavas, Goran
    Unsupervised 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.
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    Exploring Unsupervised Features in Conditional Random Fields for Spanish Named Entity Recognition
    (Institute of Electrical and Electronics Engineers Inc., 2017) Copara Zea, Jenny; Ochoa, Jose; Thorne, Camilo; Glavas, Goran
    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.
Contacto
Jorge Luis Román Yauri
Correo
jroman@ucsp.edu.pe
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