%0 Journal Article
%T Maximum Entropy-Based Chinese Word Sense Disambiguation<br>基于最大熵原理的汉语词义消歧
%A CHEN Xiao-rong
%A QIN Jin
%A <br>陈笑蓉
%A 秦进
%J 计算机科学
%D 2005
%I 
%X Word sense disambiguation is a crucial problem to be solved in NLP. A supervised machine learning method is proposed in this paper, which is applied in word disambiguation in Chinese. The method combines various features in context to disambiguate word senses. The features include annotations of words, parts of speech and sub- jects etc. And a uniform representation formalizes the features. In this way, the problem of synthesis among various features and knowledge representation of feature will be solved. 20 Chinese polysemous words are tested in our exper- iment. The result with average precision 87% shows that the method is effective.
%K Word sense disambiguation
%K Maximum entropy models
%K Supervised machine learning<br>词义消歧
%K 最大熵原理
%K 汉语
%K 自然语言处理
%K 机器学习方法
%K 最大熵模型
%K 关键问题
%K 知识表示
%K 特征
%K 上下文
%K 规范化
%K 多义词
%K 正确率
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=55B49C8E1F664792&yid=2DD7160C83D0ACED&vid=9971A5E270697F23&iid=94C357A881DFC066&sid=0584DB487B4581F4&eid=5BC9492E1D772407&journal_id=1002-137X&journal_name=计算机科学&referenced_num=3&reference_num=6