Paper: Mining Inter-Entity Semantic Relations Using Improved Transductive Learning

ACL ID I05-1036
Title Mining Inter-Entity Semantic Relations Using Improved Transductive Learning
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2005
Authors
  • Zhu Zhang (University of Michigan, Ann Arbor MI)

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. Dif- ferent 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 im- proved by using an induced similarity function.