%0 Journal Article
%T Comparing features combination with features fusion in Chinese named entity recognition<br>中文名实体识别中的特征组合与特征融合的比较
%A ZHAO Jian
%A WANG Xiao-long
%A GUAN Yi
%A <br>赵健
%A 王晓龙
%A 关毅
%J 计算机应用
%D 2005
%I 
%X Maximum entropy model is usually used for named entity recognition, in which the features related to a random event are linearly combined. The problem of the weight bias in the features combination was pointed out, and a strategy of performing features fusion before linearly combining was proposed. The result of experiment on corpus containing 2000 human names shows that features fusion can improve the precision and recall of named entity recognition effectively.
%K named entity recognition(NER)
%K features combination
%K weight bias
%K features fusion
%K maximum entropy model<br>名实体识别
%K 特征组合
%K 权值偏置
%K 特征融合
%K 最大熵模型
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=D6CC881A5163BF8C&yid=2DD7160C83D0ACED&vid=C5154311167311FE&iid=708DD6B15D2464E8&sid=28B960025A4F05E0&eid=0C3D4539B8189F4D&journal_id=1001-9081&journal_name=计算机应用&referenced_num=1&reference_num=12