%0 Journal Article
%T Scaling Conditional Random Fields by One-Against-the-Other Decomposition
%A Hai Zhao
%A Chunyu Kie
%A <br>Hai
%A Zhao
%A and
%A Chunyu
%A Kit
%J 计算机科学技术学报
%D 2008
%I 
%X As a powerful sequence labeling model,conditional random fields (CRFs) have had successful applications in many natural language processing (NLP) tasks.However,the high complexity of CRFs training only allows a very small tag (or label) 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.An optimal tag sequence is then produced by a joint decoding algorithm based on the probabilistic output of all sub-CRFs involved.To test its effectiveness,we apply this approach to tackling Chinese word segmentation (CWS) as a sequence labeling problem.Our evaluation shows that it can reduce the computational cost of this language processing task by 40-50% without any significant performance loss on various large-scale data sets.
%K natural language processing
%K machine learning
%K conditional random fields
%K Chinese word segmentation<br>
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=F57FEF5FAEE544283F43708D560ABF1B&aid=047E444F172929805A5C90F6D321C137&yid=67289AFF6305E306&vid=EA389574707BDED3&iid=E158A972A605785F&sid=EF9E84B2DA79FF23&eid=06D504E5261AB652&journal_id=1000-9000&journal_name=计算机科学技术学报&referenced_num=1&reference_num=1