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K-Similar Conditional Random Fields for Semi-supervised Sequence Labeling

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3 Author(s)
Xi Chen ; Comput. Sch., Wuhan Univ., Wuhan ; Shihong Chen ; Kun Xiao

Sequence labeling tasks, such as named entity recognition and part of speech tagging, are the fundamental compositions of the information extraction system, and thus received attentions these years. This paper proposes k-similar conditional random fields for semi-supervised sequence labeling, and makes use of unlabeled data to calculate the similarity between words with distributional clustering. The named entity recognition experiments show that this method can improve the performance through unlabeled data.

Published in:

Advanced Language Processing and Web Information Technology, 2008. ALPIT '08. International Conference on

Date of Conference:

23-25 July 2008