Event relation acquisition using dependency patterns and confidence-weighted co-occurrence statistics

Abstract

Event relation knowledge is important for deep language understanding and inference. Previous work has established automatic acquisition methods of event relations that focus on common sense knowledge acquisition from large-scale unlabeled corpus. However, in the case of domain-specific knowledge acquisition, such a method can not acquire much knowledge due to the limited amount of available knowledge sources. We propose an coverage-oriented acquisition method of event relations. The proposed method utilizes various patterns of dependency structures co-occurring with event relations than the existing method relying only on direct dependency relations between events. Experimental results show that the proposed method can acquire a larger amount of positive relation instances while keeping higher precision compared with the existing method and the proposed method also performs well for small sizes of corpora.