Title

Multi-Task Transfer Learning for Weakly-Supervised Relation Extraction

Publication Type

Conference Proceeding Article

Publication Date

8-2009

Abstract

Creating labeled training data for relation extraction is expensive. In this paper, we study relation extraction in a special weakly-supervised setting when we have only a few seed instances of the target relation type we want to extract but we also have a large amount of labeled instances of other relation types. Observing that different relation types can share certain common structures, we propose to use a multi-task learning method coupled with human guidance to address this weakly-supervised relation extraction problem. The proposed framework models the commonality among different relation types through a shared weight vector, enables knowledge learned from the auxiliary relation types to be transferred to the target relation type, and allows easy control of the tradeoff between precision and recall. Empirical evaluation on the ACE 2004 data set shows that the proposed method substantially improves over two baseline methods.

Discipline

Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (ACL-IJCNLP '09)

First Page

1012

Last Page

1020

City or Country

Singapore

Additional URL

http://www.aclweb.org/anthology/P/P09/P09-1114.pdf