计算机科学
领域(数学)
深度学习
人工智能
理解力
数据科学
钥匙(锁)
阅读(过程)
深层神经网络
机器学习
自然语言处理
数学
计算机安全
政治学
纯数学
法学
程序设计语言
作者
Xin Su,Zhen Huang,Yunxiang Zhao,Yifan Chen,Yong Dou,Hengyue Pan
出处
期刊:IEEE/ACM transactions on audio, speech, and language processing
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:31: 2765-2786
被引量:2
标识
DOI:10.1109/taslp.2023.3254166
摘要
Emotion Cause Extraction Field (ECEF) focuses on the cause that triggers an emotion in a document and mainly includes Emotion Cause Extraction (ECE) and Emotion Cause Pair Extraction (ECPE). Traditional ECE aims to extract the cause based on a given emotion while ECPE aims to extract both the emotion and its corresponding cause. Recently, ECEF has attracted a lot of attention and most of the advances have benefited from significant developments in deep learning techniques, especially machine reading comprehension and neural-network-based information retrieval. The large pre-trained language model of BERT has also shown effectiveness in this field. Following the proposal of ECPE, the development of ECEF has accelerated. However, a comprehensive review of existing approaches and recent trends in the field is lacking. To address this issue, this survey presents a thorough review to summarise existing methods and recent key advances, illustrate the general technical architecture of traditional ECE, introduce several important variants, in particular ECPE, and provide a detailed comparison of several public datasets. Finally, the limitations of existing work and the prospects for further technological advances in ECEF are discussed.
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