Knowledge Commons of Institute of Automation,CAS
Interpreting Sentiment Composition with Latent Semantic Tree | |
Zhongtao Jiang1,2![]() ![]() ![]() ![]() | |
2023-07-09 | |
会议名称 | Findings of the Association for Computational Linguistics: ACL 2023 |
会议日期 | 2023-7-9 |
会议地点 | Toronto, Canada |
摘要 | As the key to sentiment analysis, sentiment composition considers the classification of a constituent via classifications of its contained sub-constituents and rules operated on them. Such compositionality has been widely studied previously in the form of hierarchical trees including untagged and sentiment ones, which are intrinsically suboptimal in our view. To address this, we propose semantic tree, a new tree form capable of interpreting the sentiment composition in a principled way. Semantic tree is a derivation of a context-free grammar (CFG) describing the specific composition rules on difference semantic roles, which is designed carefully following previous linguistic conclusions. However, semantic tree is a latent variable since there is no its annotation in regular datasets. Thus, in our method, it is marginalized out via inside algorithm and learned to optimize the classification performance. Quantitative and qualitative results demonstrate that our method not only achieves better or competitive results compared to baselines in the setting of regular and domain adaptation classification, and also generates plausible tree explanations. |
语种 | 英语 |
七大方向——子方向分类 | 自然语言处理 |
国重实验室规划方向分类 | 语音语言处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57262 |
专题 | 复杂系统认知与决策实验室 |
作者单位 | 1.The Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.Meituan |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhongtao Jiang,Yuanzhe Zhang,Cao Liu,et al. Interpreting Sentiment Composition with Latent Semantic Tree[C],2023. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Interpreting Sentime(509KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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