Better Queries for Aspect-Category Sentiment Classification
Li Yuncong, Yin Cunxiang, Zhong Sheng-hua, Zhong Huiqiang, Luo Jinchang, Xu Siqi, Wu Xiaohui
Abstract
Aspect-category sentiment classification (ACSC) aims to identify the sentiment polarities towards the aspect categories mentioned in a sentence. Because a sentence often mentions more than one aspect category and expresses different sentiment polarities to them, finding aspect category-related information from the sentence is the key challenge to accurately recognize the sentiment polarity. Most previous models take both sentence and aspect category as input and query aspect category-related information based on the aspect category. However, these models represent the aspect category as a context-independent vector called aspect embedding, which may not be effective enough as a query. In this paper, we propose two contextualized aspect category representations, Contextualized Aspect Vector (CAV) and Contextualized Aspect Matrix (CAM). Specifically, we use the coarse aspect category-related information found by the aspect category detection task to generate CAV or CAM. Then the CAV or CAM as queries are used to search for fine-grained aspect category-related information like aspect embedding by aspect-category sentiment classification models. In experiments, we integrate the proposed CAV and CAM into several representative aspect embedding-based aspect-category sentiment classification models. Experimental results on the SemEval-2014 Restaurant Review dataset and the Multi-Aspect Multi-Sentiment dataset demonstrate the effectiveness of CAV and CAM.- Anthology ID:
- 2020.ccl-1.100
- Volume:
- Proceedings of the 19th Chinese National Conference on Computational Linguistics
- Month:
- October
- Year:
- 2020
- Address:
- Haikou, China
- Venue:
- CCL
- SIG:
- Publisher:
- Chinese Information Processing Society of China
- Note:
- Pages:
- 1079–1088
- Language:
- English
- URL:
- https://aclanthology.org/2020.ccl-1.100
- DOI:
- Cite (ACL):
- Li Yuncong, Yin Cunxiang, Zhong Sheng-hua, Zhong Huiqiang, Luo Jinchang, Xu Siqi, and Wu Xiaohui. 2020. Better Queries for Aspect-Category Sentiment Classification. In Proceedings of the 19th Chinese National Conference on Computational Linguistics, pages 1079–1088, Haikou, China. Chinese Information Processing Society of China.
- Cite (Informal):
- Better Queries for Aspect-Category Sentiment Classification (Yuncong et al., CCL 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.ccl-1.100.pdf