TUD: Semantic Relatedness for Relation Classification
György Szarvas, Iryna Gurevych · TUbilio (Technical University of Darmstadt) · 2010
In this paper, we describe the system submitted by the team TUD to Task 8 at SemEval 2010. The challenge focused on the identification of semantic relations between pairs of nominals in sentences collected from the web. We applied maximum entropy classification using both lexical and syntactic features to describe the nominals and their context. In addition, we experimented with features describing the semantic relatedness (SR) between the target nominals and a set of clue words characteristic to the relations. Our best submission with SR features achieved 69.23 % macro-averaged F-measure, providing 8.73 % improvement over our baseline system. Thus, we think SR can serve as a natural way to incorporate external knowledge to relation classification. 1