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        <title>Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation</title>
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    <name type="personal">
        <namePart type="given">Xiao</namePart>
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        <dateIssued>2018-oct&quot;-&quot;nov</dateIssued>
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    <abstract>Event extraction is of practical utility in natural language processing. In the real world, it is a common phenomenon that multiple events existing in the same sentence, where extracting them are more difficult than extracting a single event. Previous works on modeling the associations between events by sequential modeling methods suffer a lot from the low efficiency in capturing very long-range dependencies. In this paper, we propose a novel Jointly Multiple Events Extraction (JMEE) framework to jointly extract multiple event triggers and arguments by introducing syntactic shortcut arcs to enhance information flow and attention-based graph convolution networks to model graph information. The experiment results demonstrate that our proposed framework achieves competitive results compared with state-of-the-art methods.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1156</identifier>
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        <date>2018-oct&quot;-&quot;nov</date>
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%0 Conference Proceedings
%T Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation
%A Liu, Xiao
%A Luo, Zhunchen
%A Huang, Heyan
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct" "nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F liu-etal-2018-jointly
%X Event extraction is of practical utility in natural language processing. In the real world, it is a common phenomenon that multiple events existing in the same sentence, where extracting them are more difficult than extracting a single event. Previous works on modeling the associations between events by sequential modeling methods suffer a lot from the low efficiency in capturing very long-range dependencies. In this paper, we propose a novel Jointly Multiple Events Extraction (JMEE) framework to jointly extract multiple event triggers and arguments by introducing syntactic shortcut arcs to enhance information flow and attention-based graph convolution networks to model graph information. The experiment results demonstrate that our proposed framework achieves competitive results compared with state-of-the-art methods.
%R 10.18653/v1/D18-1156
%U https://aclanthology.org/D18-1156
%U https://doi.org/10.18653/v1/D18-1156
%P 1247-1256

Markdown (Informal)

[Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation](https://aclanthology.org/D18-1156) (Liu et al., EMNLP 2018)

• Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation (Liu et al., EMNLP 2018)

ACL

• Xiao Liu, Zhunchen Luo, and Heyan Huang. 2018. Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1247–1256, Brussels, Belgium. Association for Computational Linguistics.