Event Detection without Triggers

Shulin Liu, Yang Li, Feng Zhang, Tao Yang, Xinpeng Zhou

Abstract
The goal of event detection (ED) is to detect the occurrences of events and categorize them. Previous work solved this task by recognizing and classifying event triggers, which is defined as the word or phrase that most clearly expresses an event occurrence. As a consequence, existing approaches required both annotated triggers and event types in training data. However, triggers are nonessential to event detection, and it is time-consuming for annotators to pick out the “most clearly” word from a given sentence, especially from a long sentence. The expensive annotation of training corpus limits the application of existing approaches. To reduce manual effort, we explore detecting events without triggers. In this work, we propose a novel framework dubbed as Type-aware Bias Neural Network with Attention Mechanisms (TBNNAM), which encodes the representation of a sentence based on target event types. Experimental results demonstrate the effectiveness. Remarkably, the proposed approach even achieves competitive performances compared with state-of-the-arts that used annotated triggers.
Anthology ID:
N19-1080
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
735–744
Language:
URL:
https://aclanthology.org/N19-1080
DOI:
10.18653/v1/N19-1080
Bibkey:
Cite (ACL):
Shulin Liu, Yang Li, Feng Zhang, Tao Yang, and Xinpeng Zhou. 2019. Event Detection without Triggers. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 735–744, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Event Detection without Triggers (Liu et al., NAACL 2019)
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PDF:
https://preview.aclanthology.org/nschneid-patch-2/N19-1080.pdf
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