Corpus-based lexical choice in natural language generation

被引：0
|
作者：
Bangalore, S [1 ]
Rambow, O [1 ]
机构：
[1] AT&T Labs Res, Florham Pk, NJ 07932 USA
来源：
38TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE | 2000年
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：
Choosing the best lexeme to realize a meaning in natural language generation is a hard task. We investigate different tree-based stochastic models for lexical choice. Because of the difficulty of obtaining a sense-tagged corpus, we generalize the notion of synonymy. We show that a tree-based model can achieve a word-bag based accuracy of 90%, representing an improvement over the baseline.
引用
收藏
页码：464 / 471
页数：8
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