research-article

A Multi-channel Hierarchical Graph Attention Network for Open Event Extraction

Authors:

Dexi LiuAuthors Info & Claims

ACM Transactions on Information Systems, Volume 41, Issue 1

Article No.: 20, Pages 1 - 27

https://doi.org/10.1145/3528668

Published: 10 January 2023 Publication History

Abstract

Event extraction is an essential task in natural language processing. Although extensively studied, existing work shares issues in three aspects, including (1) the limitations of using original syntactic dependency structure, (2) insufficient consideration of the node level and type information in Graph Attention Network (GAT), and (3) insufficient joint exploitation of the node dependency type and part-of-speech (POS) encoding on the graph structure. To address these issues, we propose a novel framework for open event extraction in documents. Specifically, to obtain an enhanced dependency structure with powerful encoding ability, our model is capable of handling an enriched parallel structure with connected ellipsis nodes. Moreover, through a bidirectional dependency parsing graph, it considers the sequence of order structure and associates the ancestor and descendant nodes. Subsequently, we further exploit node information, such as the node level and type, to strengthen the aggregation of node features in our GAT. Finally, based on the coordination of triple-channel features (i.e., semantic, syntactic dependency and POS), the performance of event extraction is significantly improved. Extensive experiments are conducted to validate the effectiveness of our method, and the results confirm its superiority over the state-of-the-art baselines. Furthermore, in-depth analyses are provided to explore the essential factors determining the extraction performance.

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Cited By

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Wan QWan CXiao KLu KLi CLiu XLiu DWooldridge MDy JNatarajan S(2024)Dependency structure-enhanced graph attention networks for event detectionProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i17.29877(19098-19106)Online publication date: 20-Feb-2024
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Index Terms

A Multi-channel Hierarchical Graph Attention Network for Open Event Extraction
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 41, Issue 1

January 2023

759 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3570137

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 10 January 2023

Online AM: 22 April 2022

Accepted: 24 March 2022

Revised: 09 February 2022

Received: 04 August 2021

Published in TOIS Volume 41, Issue 1

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National Natural Science Foundation of China
Science & Technology Project of the Department of Education of Jiangxi Province
Jiangxi Province Graduate Innovation Special Fund Project
Natural Science and Foundation of Jiangxi Province
Funding Program for Academic and Technical Leaders in Major Disciplines of Jiangxi Province

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Wan QWan CXiao KLu KLi CLiu XLiu DWooldridge MDy JNatarajan S(2024)Dependency structure-enhanced graph attention networks for event detectionProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i17.29877(19098-19106)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i17.29877
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Zhao YFei HCao YLi BZhang MWei JZhang MChua TEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Constructing Holistic Spatio-Temporal Scene Graph for Video Semantic Role LabelingProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612096(5281-5291)Online publication date: 26-Oct-2023
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