Paper: Modeling Commonality Among Related Classes In Relation Extraction

ACL ID P06-1016
Title Modeling Commonality Among Related Classes In Relation Extraction
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

This paper proposes a novel hierarchical learn- ing strategy to deal with the data sparseness problem in relation extraction by modeling the commonality among related classes. For each class in the hierarchy either manually prede- fined or automatically clustered, a linear dis- criminative function is determined in a top- down way using a perceptron algorithm with the lower-level weight vector derived from the upper-level weight vector. As the upper-level class normally has much more positive train- ing examples than the lower-level class, the corresponding linear discriminative function can be determined more reliably. The upper- level discriminative function then can effec- tively guide the discriminative function learn- ing in the lower-level, which otherwise might suffer from limited t...