Scaling conditional random field with application to chinese word segmentation

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 learning. Instead of training a single CRF model for all tags, it trains a binary sub-CRF independently for each tag. A predicted tag sequence is then produced by a joint decoding algorithm based on the probabilistic output of all sub-CRFs involved. To test its effectiveness, this approach is applied to tackle Chinese word segmentation (CWS) as a character tagging problem. Our evaluation shows that it can reduce time and memory cost by 20-39% and 44-50%, respectively, without any significant performance loss on various large-scale data sets. © 2007 IEEE.