TY - GEN
T1 - Overview of the SIGHAN 2024 Shared Task for Chinese Dimensional Aspect-Based Sentiment Analysis
AU - Lee, Lung Hao
AU - Yu, Liang Chih
AU - Wang, Suge
AU - Liao, Jian
N1 - Publisher Copyright:
© 2024 Association for Computational Linguistics
PY - 2024
Y1 - 2024
N2 - This paper describes the SIGHAN-2024 shared task for Chinese dimensional aspect-based sentiment analysis (ABSA), including task description, data preparation, performance metrics, and evaluation results. Compared to representing affective states as several discrete classes (i.e., sentiment polarity), the dimensional approach represents affective states as continuous numerical values (called sentiment intensity) in the valence-arousal space, providing more fine-grained affective states. Therefore, we organized a dimensional ABSA (shorted dimABSA) shared task, comprising three subtasks: 1) intensity prediction, 2) triplet extraction, and 3) quadruple extraction, receiving a total of 214 submissions from 61 registered participants during evaluation phase. A total of eleven teams provided selected submissions for each subtask and seven teams submitted technical reports for the subtasks. This shared task demonstrates current NLP techniques for dealing with Chinese dimensional ABSA. All data sets with gold standards and evaluation scripts used in this shared task are publicly available for future research.
AB - This paper describes the SIGHAN-2024 shared task for Chinese dimensional aspect-based sentiment analysis (ABSA), including task description, data preparation, performance metrics, and evaluation results. Compared to representing affective states as several discrete classes (i.e., sentiment polarity), the dimensional approach represents affective states as continuous numerical values (called sentiment intensity) in the valence-arousal space, providing more fine-grained affective states. Therefore, we organized a dimensional ABSA (shorted dimABSA) shared task, comprising three subtasks: 1) intensity prediction, 2) triplet extraction, and 3) quadruple extraction, receiving a total of 214 submissions from 61 registered participants during evaluation phase. A total of eleven teams provided selected submissions for each subtask and seven teams submitted technical reports for the subtasks. This shared task demonstrates current NLP techniques for dealing with Chinese dimensional ABSA. All data sets with gold standards and evaluation scripts used in this shared task are publicly available for future research.
UR - http://www.scopus.com/inward/record.url?scp=85200806062&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85200806062
T3 - SIGHAN 2024 - 10th SIGHAN Workshop on Chinese Language Processing, Proceedings of the Workshop
SP - 165
EP - 174
BT - SIGHAN 2024 - 10th SIGHAN Workshop on Chinese Language Processing, Proceedings of the Workshop
A2 - Wong, Kam-Fai
A2 - Zhang, Min
A2 - Xu, Ruifeng
A2 - Li, Jing
A2 - Wei, Zhongyu
A2 - Gui, Lin
A2 - Liang, Bin
A2 - Zhao, Runcong
PB - Association for Computational Linguistics (ACL)
T2 - 10th SIGHAN Workshop on Chinese Language Processing, SIGHAN 2024
Y2 - 16 August 2024
ER -