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As a powerful sequence labeling model, conditional random field (CRF) has been applied to a number of natural language processing (NLP) tasks successfully. However, the high complexity of CRF training only allows a very small tag (or label)1 set, because the training becomes intractable as the tag set enlarges. This paper proposes an improved decomposed training and joint decoding algorithm for CRF...
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