摘要
【目的】提出基于多标签Seq2Seq模型的情绪-原因对提取方法,提高情绪-原因对抽取效果。【方法】使用BERT预训练得到语义丰富的词向量,通过Bi-GRU和LSTM进行编码分别得到文本的全局特征和局部特征,引入混合注意力机制实现二者的融合,提高文本语义特征捕获的完整度。【结果】相较于FSS-GCN模型,本文模型对情绪-原因对的联合抽取F1值在两个数据集上分别提升0.98个百分点和11.60个百分点,情绪抽取子任务分别提升0.87个百分点和1.10个百分点,原因抽取子任务分别提升0.79个百分点和2.31个百分点。【局限】模型主要考虑显式情绪-原因对,未针对隐式情绪-原因对进行探讨。【结论】本文提出的模型能提高情绪-原因对抽取效果。
[Objective]This paper explores new algorithms to extract emotion-cause pairs based on multi-label Seq2Seq model.[Methods]First,we used the BERT pre-training to obtain semantically rich word vectors.Then,we utilized the Bi-GRU and LSTM to obtain the global and local features of the texts.Finally,we introduced the hybrid attention mechanism to merge the features and improve the integrity of these semantic features.[Results]Compared with the latest FSS-GCN model,the F1 value of our new model for emotional cause pairs increased by 0.98 percentage point and 11.60 percentage point on two data sets.The F1 value of emotion extraction increased by 0.87 percentage point and 1.10 percentage point,while the F1 value for cause extraction increased by 0.79percentage point and 2.31 percentage point respectively.[Limitations]Our new model mainly examined the explicit emotion-cause pairs and did not explore implicit emotion-cause pairs.[Conclusions]The proposed model improves the F1 values of extracting emotion-cause pairs.
作者
张思阳
魏苏波
孙争艳
张顺香
朱广丽
吴厚月
Zhang Siyang;Wei Subo;Sun Zhengyan;Zhang Shunxiang;Zhu Guangli;Wu Houyue(School of Mathematics and Big Data,Anhui University of Science and Technology,Huainan 232001,China;School of Computer Science and Engineering,Anhui University of Science and Technology,Huainan 232001,China;Artificial Intelligence Research Institute,Hefei Comprehensive National Science Center,Hefei 230026,China;School of Computer Science,Huainan Normal University,Huainan 232038,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2023年第2期86-96,共11页
Data Analysis and Knowledge Discovery
基金
国家自然科学基金项目(项目编号:62076006)
安徽省属高校协同创新项目(项目编号:GXXT-2021-008)
安徽省自然科学基金项目(项目编号:1908085MF189)的研究成果之一。
