Paper: On Learning Subtypes of the Part-Whole Relation: Do Not Mix Your Seeds

ACL ID P10-1135
Title On Learning Subtypes of the Part-Whole Relation: Do Not Mix Your Seeds
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

An important relation in information ex- traction is the part-whole relation. On- tological studies mention several types of this relation. In this paper, we show that the traditional practice of initializ- ing minimally-supervised algorithms with a single set that mixes seeds of different types fails to capture the wide variety of part-whole patterns and tuples. The re- sults obtained with mixed seeds ultimately converge to one of the part-whole relation types. We also demonstrate that all the different types of part-whole relations can still be discovered, regardless of the type characterized by the initializing seeds. We performed our experiments with a state-of- the-art information extraction algorithm.