@inproceedings{liu-etal-2019-event,
title = "Event Detection without Triggers",
author = "Liu, Shulin and
Li, Yang and
Zhang, Feng and
Yang, Tao and
Zhou, Xinpeng",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1080",
doi = "10.18653/v1/N19-1080",
pages = "735--744",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Event Detection without Triggers
%A Liu, Shulin
%A Li, Yang
%A Zhang, Feng
%A Yang, Tao
%A Zhou, Xinpeng
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S 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)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F liu-etal-2019-event
%X 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.
%R 10.18653/v1/N19-1080
%U https://aclanthology.org/N19-1080
%U https://doi.org/10.18653/v1/N19-1080
%P 735-744
Markdown (Informal)
[Event Detection without Triggers](https://aclanthology.org/N19-1080) (Liu et al., NAACL 2019)
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.