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2020 | OriginalPaper | Chapter

Event Detection with Document Structure and Graph Modelling

Authors : Peipei Zhu, Zhongqing Wang, Hongling Wang, Shoushan Li, Guodong Zhou

Published in: Natural Language Processing and Chinese Computing

Publisher: Springer International Publishing

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Abstract

Event detection is the basic task of event extraction. Previous studies usually used independent sentences as basic event detection objects. They cannot effectively identify event triggers which depend on document information. Besides, there are correlations between the sentences and words in the document. Therefore, it is necessary to use document information for event detection. In this study, we propose a graph model for event detection based on document structure. It is used to connect sentences and words in a document. Specifically, we finetune BERT model and use Bi-LSTM to learn the sentences and their context features, and then use GCN to model the document relation graph. The document relation graph is based on the parts of speech of all words in different sentences, which contributes to establishing the triggers-triggers relation and triggers-arguments relation. The experimental results on LitBank show that our proposed model outperforms all baselines significantly and verifies the validity of document information.

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previous chapter Incorporating Temporal Cues and AC-GCN to Improve Temporal Relation Classification
next chapter AFPun-GAN: Ambiguity-Fluency Generative Adversarial Network for Pun Generation
Footnotes
1
English BERT-Base: 12-layer, 768-hidden, 12-heads, 110M parameters.
 
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Metadata
Title
Event Detection with Document Structure and Graph Modelling
Authors
Peipei Zhu
Zhongqing Wang
Hongling Wang
Shoushan Li
Guodong Zhou
Copyright Year
2020
Book
Natural Language Processing and Chinese Computing

Print ISBN: 978-3-030-60449-3

Electronic ISBN: 978-3-030-60450-9

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-60450-9_47

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