Mining inter-entity semantic relations using improved transductive learning

Authors:
Zhu Zhang
Affiliations:
School of Information and Department of EECS, University of Michigan, Ann Arbor, MI
Venue:
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Year:
2005

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Abstract

This paper studies the problem of mining relational data hidden in natural language text. In particular, it approaches the relation classification problem with the strategy of transductive learning. Different algorithms are presented and empirically evaluated on the ACE corpus. We show that transductive learners exploiting various lexical and syntactic features can achieve promising classification performance. More importantly, transductive learning performance can be significantly improved by using an induced similarity function.