Spanish NER with Word Representations and Conditional Random Fields

Abstract

Word Representations such as word embeddings have been shown to significantly improve (semi-)supervised NER for the English language. In this work we investigate whether word representations can also boost (semi-)supervised NER in Spanish. To do so, we use word representations as additional features in a linear chain Conditional Random Field (CRF) classifier. Experimental results (82.44 Fscore on the CoNLL-2002 corpus) show that our approach is comparable to some state-of-the-art Deep Learning approaches for Spanish, in particular when using cross-lingual Word Representations.