Semantic Decision Internal-Attention Graph Convolutional Network for End-to-End Emotion-Cause Pair Extraction

Emotion-cause pair extraction is an emergent natural language processing task; the target is to extract all pairs of emotion clauses and corresponding cause clauses from unannotated emotion text. Previous studies have employed two-step approaches. However, this research may lead to error propagation...

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Bibliographic Details
Published in:	International journal on semantic web and information systems 2023-01, Vol.19 (1), p.1-21
Main Authors:	Zhang, Dianyuan, Zhu, Zhenfang, Qi, Jiangtao, Zhang, Guangyuan, Zhong, Linghui
Format:	Article
Language:	eng
Subjects:	Analysis Artificial neural networks Computational linguistics Emotions Error correction Graph neural networks Language processing Natural language interfaces Natural language processing Neural networks Semantics Structured data
Online Access:	Get full text
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Summary:	Emotion-cause pair extraction is an emergent natural language processing task; the target is to extract all pairs of emotion clauses and corresponding cause clauses from unannotated emotion text. Previous studies have employed two-step approaches. However, this research may lead to error propagation across stages. In addition, previous studies did not correctly handle the situation where emotion clauses and cause clauses are the same clauses. To overcome these issues, the authors first use a multitask learning model that is based on graph from the perspective of sorting, which can simultaneously extract emotion clauses, cause clauses and emotion-cause pairs via an end-to-end strategy. Then the authors propose to convert text into graph structured data, and process this scenario through a unique graph convolutional neural network. Finally, the authors design a semantic decision mechanism to address the scenario in which there are multiple emotion-cause pairs in a text.
ISSN:	1552-6283 1552-6291