Entity relationship extraction based on potential relationship pattern

Authors:
Chen Chen;HuiLin Liu;GuoRen Wang;LinLin Ding;LiLi Yu
Affiliations:
Dept of Computer Science and Technology, Northeastern University, Shenyang, China;Dept of Computer Science and Technology, Northeastern University, Shenyang, China;Dept of Computer Science and Technology, Northeastern University, Shenyang, China;Dept of Computer Science and Technology, Northeastern University, Shenyang, China;Mobile Internet Business Unit, Neusoft Corporation, Shenyang, China
Venue:
WISM'10 Proceedings of the 2010 international conference on Web information systems and mining
Year:
2010

Citing 8
Cited 0

A maximum entropy approach to natural language processing

Computational Linguistics
Kernel methods for relation extraction

The Journal of Machine Learning Research
Dependency tree kernels for relation extraction

ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations

ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Exploring various knowledge in relation extraction

ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A shortest path dependency kernel for relation extraction

HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
On-demand information extraction

COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
A novel feature-based approach to Chinese entity relation extraction

HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers

Quantified Score

Hi-index	0.00

Visualization

Abstract

The keep rising of web information ensures the development of entity focused information retrieval system. However, the problem of mining the relationships effectively between entities has not been well resolved. For the entity relationship extraction (RE) problem, this paper firstly establishes the basic pattern trees which can present the overall relation structures and then designs a similarity function according to which we can judge which pattern the sentence containing two entities belongs to. Knowing the matched pattern, we can discovery the relationship easily. By a large number of experiments on real data, the proposed methods are proved running accurately and efficiently.