Paper: A Systematic Exploration of the Feature Space for Relation Extraction

ACL ID N07-1015
Title A Systematic Exploration of the Feature Space for Relation Extraction
Venue Human Language Technologies
Session Main Conference
Year 2007
Authors

Relation extraction is the task of find- ing semantic relations between entities from text. The state-of-the-art methods for relation extraction are mostly based on statistical learning, and thus all have to deal with feature selection, which can significantly affect the classification per- formance. In this paper, we systemat- ically explore a large space of features for relation extraction and evaluate the ef- fectiveness of different feature subspaces. We present a general definition of fea- ture spaces based on a graphic represen- tation of relation instances, and explore three different representations of relation instances and features of different com- plexities within this framework. Our ex- periments show that using only basic unit features is generally sufficient to achieve state...