Title

Extracting temporal and causal relations based on event networks

Document Type

Article

Source of Publication

Information Processing and Management

Publication Date

11-1-2020

Abstract

© 2020 Elsevier Ltd Event relations specify how different event flows expressed within the context of a textual passage relate to each other in terms of temporal and causal sequences. There have already been impactful work in the area of temporal and causal event relation extraction; however, the challenge with these approaches is that (1) they are mostly supervised methods and (2) they rely on syntactic and grammatical structure patterns at the sentence-level. In this paper, we address these challenges by proposing an unsupervised event network representation for temporal and causal relation extraction that operates at the document level. More specifically, we benefit from existing Open IE systems to generate a set of triple relations that are then used to build an event network. The event network is bootstrapped by labeling the temporal disposition of events that are directly linked to each other. We then systematically traverse the event network to identify the temporal and causal relations between indirectly connected events. We perform experiments based on the widely adopted TempEval-3 and Causal-TimeBank corpora and compare our work with several strong baselines. We show that our method improves performance compared to several strong methods.

ISSN

0306-4573

Publisher

Elsevier Ltd

Volume

57

Issue

6

First Page

102319

Disciplines

Computer Sciences

Keywords

Causal event, Event extraction, Event network, Open information extraction, Temporal event

Scopus ID

85086579555

Indexed in Scopus

yes

Open Access

no

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