Title

A Systematic Exploration of the Feature Space for Relation Extraction

Publication Type

Conference Proceeding Article

Publication Date

4-2007

Abstract

Relation extraction is the task of finding semantic relations between entities from text. The state-of-the-art methods for relation extraction are mostly based on statistical learning, and thus all have to deal with feature selection, which can significantly affect the classification performance. In this paper, we systematically explore a large space of features for relation extraction and evaluate the effectiveness of different feature subspaces. We present a general definition of feature spaces based on a graphic representation of relation instances, and explore three different representations of relation instances and features of different complexities within this framework. Our experiments show that using only basic unit features is generally sufficient to achieve state-of-the-art performance, while overinclusion of complex features may hurt the performance. A combination of features of different levels of complexity and from different sentence representations, coupled with task-oriented feature pruning, gives the best performance.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

Human language technologies 2007: The conference of the North American Chapter of the Association for Computational Linguistics; 22 - 27 April 2007, Rochester, New York

First Page

113

Last Page

120

ISBN

9781932432916

Publisher

ACL

City or Country

Rochester, NY, USA

Additional URL

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.80.7503

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