Paper: Acquiring Event Relation Knowledge by Learning Cooccurrence Patterns and Fertilizing Cooccurrence Samples with Verbal Nouns

ACL ID I08-1065
Title Acquiring Event Relation Knowledge by Learning Cooccurrence Patterns and Fertilizing Cooccurrence Samples with Verbal Nouns
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2008
Authors

Aiming at acquiring semantic relations be- tween events from a large corpus, this paper proposes several extensions to a state-of-the- art method originally designed for entity re- lation extraction, reporting on the present re- sults of our experiments on a Japanese Web corpus. The results show that (a) there are indeed specific cooccurrence patterns use- ful for event relation acquisition, (b) the use of cooccurrence samples involving ver- bal nouns has positive impacts on both re- call and precision, and (c) over five thou- sand relation instances are acquired from a 500M-sentence Web corpus with a precision of about 66% for action-effect relations.