| 引用本文: | 叶苑莉,张灵,陈云华.基于事件语境的文本情感原因对特征提取[J].控制理论与应用,2022,39(7):1315~1323.[点击复制] |
| YE Yuan-li,ZHANG Ling,CHEN Yun-hua.Feature extraction of emotion-cause pairs in text based on event context[J].Control Theory and Technology,2022,39(7):1315~1323.[点击复制] |
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| 基于事件语境的文本情感原因对特征提取 |
| Feature extraction of emotion-cause pairs in text based on event context |
| 摘要点击 1017 全文点击 330 投稿时间：2021-05-16 修订日期：2022-03-21 |
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| DOI编号 10.7641/CTA.2021.10408 |
| 2022,39(7):1315-1323 |
| 中文关键词 情感原因对 情感分析 注意力机制 事件语境 |
| 英文关键词 emotion-cause pair sentiment analysis attention mechanism event context |
| 基金项目 广东省交通运输厅科技项目(科技–2016–02–030), 智慧交通跨域关联大数据挖掘与指导决策关键技术研究与应用基金项目(20151BAB207043), 广东省自然科学基金项目(2021A1515012233)资助 |
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| 中文摘要 |
| 现有的情感原因对提取任务(ECPE)大多采用将情感从句逐一与原因从句匹配的方法, 或专注于候选对的
排序方法, 忽略了影响情感因果关系成立的从句的事件语境, 导致模型在理解情感因果关系时产生偏差, 并且无法
捕捉长距离的因果关系. 为此, 本文提出了基于注意力机制和情感从句卷积核的分层模型, 将原始文档的事件语境
特征嵌入到情感原因对特征提取器中, 以创建一个集成和增强的特征. 首先, 将情感分析得到的情感从句类别特征
作为卷积核. 然后, 利用文档的事件语境特征提取情感原因对. 本文方法在中文数据集的F1分数上有1.38%6.08%
的提升, 在英文数据集的F1分数上有2.35%~7.27%的提升, 说明情感分析和因果事件语境对于情感原因对提取的
有效性. |
| 英文摘要 |
| For emotion-cause pair extraction (ECPE) task, most of the existing works only match the emotion clause
and the corresponding cause clause one by one, or focus on sorting candidate pairs, ignoring the event context among
clauses that would impact significantly on the establishment of emotion causality. This leads to the deviation of the model
in learning emotion causality. They also fail to capture long-span causality hidden among plenty of clauses. To address
this issue, we propose a hierarchical model based on attention mechanism and the convolution kernel of emotion clause. In
our method, we derive event context features from the original documents and embed them into emotion-cause pair feature
extractors to create an integrated and enhanced feature. Firstly, the category features of emotion clause obtained from
sentiment analysis are used as convolution kernel. Then, the event context features of documents are presented to extract
emotion-cause pairs. The experimental results show that the F1 score on the Chinese benchmark emotion cause corpus are
improved from 1.38% to 6.08% compared with the state-of-the-art approaches. Meanwhile, the F1 score on the English
benchmark emotion cause corpus are improved from 2.35% to 7.27% compared with the state-of-the-art approaches. They
verify the effectiveness of sentiment analysis and causal event context in ECPE. |
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