%0 Journal Article %T Scaling Conditional Random Fields by One-Against-the-Other Decomposition %A Hai Zhao %A Chunyu Kie %A
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
%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