NDLI: Scaling Conditional Random Field with Application to Chinese Word Segmentation

Content Provider	IEEE Xplore Digital Library
Author	Hai Zhao Chunyu Kit
Copyright Year	2007
Description	Author affiliation: City Univ. of Hong Kong, Kowloon (Hai Zhao; Chunyu Kit)
Abstract	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.
Starting Page	95
Ending Page	99
File Size	132962
Page Count	5
File Format	PDF
ISBN	9780769528755
DOI	10.1109/ICNC.2007.648
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Tagging Cost function Natural language processing Decoding Large-scale systems Labeling Yttrium Computational complexity Testing
Content Type	Text
Resource Type	Article

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