%0 Journal Article
%T Resolving combinational ambiguity in Chinese word segmentation based on rule mining and Naive Bayes method<br>基于规则挖掘和Naive Bayes方法的组合型歧义字段切分
%A 张严虎
%A 潘璐璐
%A 彭子平
%A 张靖波
%A 于中华
%J 计算机应用
%D 2008
%I 
%X Combinational ambiguity is one of the most difficult problems for Chinese word segmentation. After in-depth analysis of the other algorithms in literature, the paper proposed a new segmentation algorithm. The algorithm automatically mined word collocation rules and grammar rules from training corpus, and then made integrated decisions to resolve combinational ambiguity based on the mined rules and Naive Bayes method. Extensive experiments show that the proposed algorithm obtains an accuracy increase of 8% against the related works.
%K 中文分词
%K 组合型歧义
%K 词语搭配规则
%K 语法规则
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=E0B60DAF9A5E83A4E8F3C23838381FE0&yid=67289AFF6305E306&vid=D3E34374A0D77D7F&iid=DF92D298D3FF1E6E&sid=0D56350A4FA1FCC9&eid=57133673016E56C3&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=7