Publication Type

Conference Proceeding Article

Publication Date

7-2011

Abstract

We address the task of automatic discovery of information extraction template from a given text collection. Our approach clusters candidate slot fillers to identify meaningful template slots. We propose a generative model that incorporates distributional prior knowledge to help distribute candidates in a document into appropriate slots. Empirical results suggest that the proposed prior can bring substantial improvements to our task as compared to a K-means baseline and a Gaussian mixture model baseline. Specifically, the proposed prior has shown to be effective when coupled with discriminative features of the candidates.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

First Page

814

Last Page

824

City or Country

Edinburgh, Scotland, UK

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