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

要旨

Aiming at acquiring semantic relations between events from a large corpus, this paper proposes several extensions to a state-of-theart method originally designed for entity relation extraction, reporting on the present results of our experiments on a Japanese Web corpus. The results show that (a) there are indeed specific cooccurrence patterns useful for event relation acquisition, (b) the use of cooccurrence samples involving verbal nouns has positive impacts on both recall and precision, and (c) over five thousand relation instances are acquired from a 500M-sentence Web corpus with a precision of about 66 % for action-effect relations. 1

主題