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        <title>Exploiting the Entity Type Sequence to Benefit Event Detection</title>
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    <name type="personal">
        <namePart type="given">Jianwei</namePart>
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    <name type="personal">
        <namePart type="given">Huaiyu</namePart>
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    <abstract>Event Detection (ED) is one of the most important task in the field of information extraction. The goal of ED is to find triggers in sentences and classify them into different event types. In previous works, the information of entity types are commonly utilized to benefit event detection. However, the sequential features of entity types have not been well utilized yet in the existing ED methods. In this paper, we propose a novel ED approach which learns sequential features from word sequences and entity type sequences separately, and combines these two types of sequential features with the help of a trigger-entity interaction learning module. The experimental results demonstrate that our proposed approach outperforms the state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T Exploiting the Entity Type Sequence to Benefit Event Detection
%A Ji, Yuze
%A Lin, Youfang
%A Gao, Jianwei
%A Wan, Huaiyu
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F ji-etal-2019-exploiting
%X Event Detection (ED) is one of the most important task in the field of information extraction. The goal of ED is to find triggers in sentences and classify them into different event types. In previous works, the information of entity types are commonly utilized to benefit event detection. However, the sequential features of entity types have not been well utilized yet in the existing ED methods. In this paper, we propose a novel ED approach which learns sequential features from word sequences and entity type sequences separately, and combines these two types of sequential features with the help of a trigger-entity interaction learning module. The experimental results demonstrate that our proposed approach outperforms the state-of-the-art methods.
%R 10.18653/v1/K19-1057
%U https://aclanthology.org/K19-1057/
%U https://doi.org/10.18653/v1/K19-1057
%P 613-623

Markdown (Informal)

[Exploiting the Entity Type Sequence to Benefit Event Detection](https://aclanthology.org/K19-1057/) (Ji et al., CoNLL 2019)

• Exploiting the Entity Type Sequence to Benefit Event Detection (Ji et al., CoNLL 2019)

ACL

• Yuze Ji, Youfang Lin, Jianwei Gao, and Huaiyu Wan. 2019. Exploiting the Entity Type Sequence to Benefit Event Detection. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 613–623, Hong Kong, China. Association for Computational Linguistics.