Enriching the knowledge sources used in a maximum entropy part-of-speech tagger

被引：416
|
作者：
Toutanova, K [1 ]
Manning, CD [1 ]
机构：
[1] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA
来源：
PROCEEDINGS OF THE 2000 JOINT SIGDAT CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND VERY LARGE CORPORA | 2000年
关键词：
D O I：
10.3115/1117794.1117802
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents results for a maximum-entropy-based part of speech tagger, which achieves superior performance principally by enriching the information sources used for tagging. In particular, we get improved results by incorporating these features: (i) more extensive treatment of capitalization for unknown words; (ii) features for the disambiguation of the tense forms of verbs; (iii) features for disambiguating particles from prepositions and adverbs. The best resulting accuracy for the tagger on the Penn Treebank is 96.86% overall, and 86.91% on previously unseen words.
引用
收藏
页码：63 / 70
页数：8
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