Multi-level boundary classification for information extraction

被引：0

作者：

Finn, A ^{[1
]}

Kushmerick, N ^{[1
]}

机构：

[1] Univ Coll Dublin, Comp Sci Dept, Smart Media Inst, Dublin, Ireland

来源：

MACHINE LEARNING: ECML 2004, PROCEEDINGS | 2004年 / 3201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：

We investigate the application of classification techniques to the problem of information extraction (IE). In particular we use support vector machines and several different feature-sets to build a set of classifiers for IE. We show that this approach is competitive with current state-of-the-art IE algorithms based on specialized learning algorithms. We also introduce a new technique for improving the recall of our IE algorithm. This approach uses a two-level ensemble of classifiers to improve the recall of the extracted fragments while maintaining high precision. We show that this approach outperforms current state-of-the-art IE algorithms on several benchmark IE tasks.

引用

页码：111 / 122

页数：12