Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
11-2021
Abstract
This paper addresses the task of span-level emotion cause analysis (SECA). It is a finer-grained emotion cause analysis (ECA) task, which aims to identify the specific emotion cause span(s) behind certain emotions in text. In this paper, we formalize SECA as a sequence tagging task for which several variants of neural network-based sequence tagging models to extract specific emotion cause span(s) in the given context. These models combine different types of encoding and decoding approaches. Furthermore, to make our models more "emotionally sensitive'', we utilize the multi-head attention mechanism to enhance the representation of context. Experimental evaluations conducted on two benchmark datasets demonstrate the effectiveness of the proposed models.
Keywords
neural network, sequence tagging, span-level emotion cause analysis
Discipline
Artificial Intelligence and Robotics | Theory and Algorithms
Research Areas
Data Science and Engineering
Publication
CIKM '21: Proceedings of the 30th ACM International Conference on Information and Knowledge Management, Virtual, November 1-5
First Page
3227
Last Page
3231
ISBN
9781450384469
Identifier
10.1145/3459637.3482186
Publisher
ACM
City or Country
New York
Citation
LI, Xiangju; GAO, Wei; FENG, Shi; WANG, Daling; and JOTY, Shafiq.
Span-level emotion cause analysis with neural sequence tagging. (2021). CIKM '21: Proceedings of the 30th ACM International Conference on Information and Knowledge Management, Virtual, November 1-5. 3227-3231.
Available at: https://ink.library.smu.edu.sg/sis_research/6688
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3459637.3482186