Task-oriented Word Embedding for Text Classification
Qian Liu, Heyan Huang, Yang Gao, Xiaochi Wei, Yuxin Tian, Luyang Liu
Abstract
Distributed word representation plays a pivotal role in various natural language processing tasks. In spite of its success, most existing methods only consider contextual information, which is suboptimal when used in various tasks due to a lack of task-specific features. The rational word embeddings should have the ability to capture both the semantic features and task-specific features of words. In this paper, we propose a task-oriented word embedding method and apply it to the text classification task. With the function-aware component, our method regularizes the distribution of words to enable the embedding space to have a clear classification boundary. We evaluate our method using five text classification datasets. The experiment results show that our method significantly outperforms the state-of-the-art methods.- Anthology ID:
- C18-1172
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2023–2032
- Language:
- URL:
- https://aclanthology.org/C18-1172
- DOI:
- Cite (ACL):
- Qian Liu, Heyan Huang, Yang Gao, Xiaochi Wei, Yuxin Tian, and Luyang Liu. 2018. Task-oriented Word Embedding for Text Classification. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2023–2032, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Task-oriented Word Embedding for Text Classification (Liu et al., COLING 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/C18-1172.pdf
- Code
- qianliu0708/ToWE
- Data
- AG News, GLUE, IMDb Movie Reviews, SST