Abstract
Weakly-supervised text classification aims to train a classifier using only class descriptions and unlabeled data. Recent research shows that keyword-driven methods can achieve state-of-the-art performance on various tasks. However, these methods not only rely on carefully-crafted class descriptions to obtain class-specific keywords but also require substantial amount of unlabeled data and takes a long time to train. This paper proposes FastClass, an efficient weakly-supervised classification approach. It uses dense text representation to retrieve class-relevant documents from external unlabeled corpus and selects an optimal subset to train a classifier. Compared to keyword-driven methods, our approach is less reliant on initial class descriptions as it no longer needs to expand each class description into a set of class-specific keywords.Experiments on a wide range of classification tasks show that the proposed approach frequently outperforms keyword-driven models in terms of classification accuracy and often enjoys orders-of-magnitude faster training speed.- Anthology ID:
- 2022.emnlp-main.313
- Original:
- 2022.emnlp-main.313v1
- Version 2:
- 2022.emnlp-main.313v2
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4746–4758
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.313
- DOI:
- 10.18653/v1/2022.emnlp-main.313
- Cite (ACL):
- Tingyu Xia, Yue Wang, Yuan Tian, and Yi Chang. 2022. FastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4746–4758, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- FastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification (Xia et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2022.emnlp-main.313.pdf