Paper Info
Title
On learning subtypes of the part-whole relation: do not mix your seeds
Abstract
An important relation in information extraction is the part-whole relation. Ontological studies mention several types of this relation. In this paper, we show that the traditional practice of initializing 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 results 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.
Year
Venue
Keywords
2010
ACL
ontological study,initializing seed,part-whole relation type,part-whole relation,minimally-supervised algorithm,state-of-the-art information extraction algorithm,part-whole pattern,different type,important relation,information extraction
Field
DocType
Volume
Ontology,Tuple,Computer science,Information extraction,Natural language processing,Artificial intelligence,Initialization,Machine learning
Conference
P10-1
Citations 
PageRank 
References 
11
0.62
20
Authors
2
Name
Order
Citations
PageRank
Ashwin Ittoo1616.58
Gosse Bouma248370.88