Identifying semantic roles using maximum entropy models

被引：0
|
作者：
Moreda, P [1 ]
Fernández, M [1 ]
Palomar, M [1 ]
Suárez, A [1 ]
机构：
[1] Univ Alicante, Dept Lenguajes & Sistemas Informat, Grp Invest Proc Lenguaje & Sistemas Informac, E-03080 Alicante, Spain
来源：
TEXT, SPEECH AND DIALOGUE, PROCEEDINGS | 2004年 / 3206卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a supervised learning method of semantic role labeling is presented. It is based on maximum entropy conditional probability models. This method acquires the linguistic knowledge from an annotated corpus and this knowledge is represented in the form of features. Several types of features have been analyzed for a few words selected from sections of the Wall Street Journal part of the Penn Treebank corpus.
引用
收藏
页码：163 / 170
页数：8
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