Research on Article Choice Based on Conditional Random Fields
Miao Xue-lei · Zhongwen xinxi xuebao · 2008
Article choice is a difficult problem for Chinese to English translation since it involves complex knowledge about grammar,semantics and the world.Traditional researches based on rule or machine learning only deal with articles used in the noun phrases.This paper considers the article as a label and hence treats the problem as a sequence labeling task,proposing a strategy based on Conditional Random Fields.In the process of feature extraction,the proposed method makes good use of the word and part-of-speech,especially the mutual information feature.Experimental results on testing corpus composed of patent abstracts containing 91 106 articles show that the algorithm yields F-score of 80%.