EnhEE: A Joint Learning Framework with Enhanced Decoding for Overlapping Event Extract
Authors: 
Hao Dong, Zhangqiao Wen, Jinyan Li, Ji Chen, Xing WangAuthors Info & Claims
Pages 671 - 675
Abstract
Event extraction aims to extract event information from unstructured texts and present it in a structured form. In overlapping event extraction tasks, the overlapping triggers and arguments in events will directly affect the performance of event extraction. This overlapping problem results in the traditional event extraction methods unable to extract and identify overlapping event sentences effectively. In response to the above problems, this paper presents an enhanced decoding joint learning framework for overlapping event extraction, EnhEE, which consists of a BERT encoder and three decoders. In the decoding part, the conditional fusion function is used to fuse the previous information, which effectively improves the performance of the model in overlapping event extraction tasks. In addition, the BiLSTM module is added in the trigger extraction and argument extraction stages, effectively improving the model's ability to obtain contextual information. In the overlapping event extraction task, EnhEE achieved F1 values of 79.4% and 72.9% in the event trigger and argument extraction tasks, respectively. The experimental results show that this method is effective on the FewFC of the public dataset, especially for the extraction of overlap events, which is a significant improvement from the previous methods.
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Index Terms
- EnhEE: A Joint Learning Framework with Enhanced Decoding for Overlapping Event Extract
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Published In
November 2023
1263 pages
ISBN:9798400708831
DOI:10.1145/3652628
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Published: 23 May 2024
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ICAICE 2023
ICAICE 2023: The 4th International Conference on Artificial Intelligence and Computer Engineering
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