Shallow semantic parsing using support vector machines

被引：0

作者：

Pradhan, S ^{[1
]}

Ward, W ^{[1
]}

Hacioglu, K ^{[1
]}

Martin, JH ^{[1
]}

Jurafsky, D ^{[1
]}

机构：

[1] Univ Colorado, Ctr Spoken Language Res, Boulder, CO 80303 USA

来源：

HLT-NAACL 2004: HUMAN LANGUAGE TECHNOLOGY CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE MAIN CONFERENCE | 2004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：

In this paper, we propose a machine learning algorithm for shallow semantic parsing, extending the work of Gildea and Jurafsky (2002), Surdeanu et al. (2003) and others. Our algorithm is based on Support Vector Machines which we show give an improvement in performance over earlier classifiers. We show performance improvements through a number of new features and measure their ability to generalize to a new test set drawn from the AQUAINT corpus.

引用

页码：233 / 240

页数：8

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