Abstract
Topics generated by topic models are typically represented as list of terms. To reduce the cognitive overhead of interpreting these topics for end-users, we propose labelling a topic with a succinct phrase that summarises its theme or idea. Using Wikipedia document titles as label candidates, we compute neural embeddings for documents and words to select the most relevant labels for topics. Comparing to a state-of-the-art topic labelling system, our methodology is simpler, more efficient and finds better topic labels.- Anthology ID:
- C16-1091
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 953–963
- Language:
- URL:
- https://aclanthology.org/C16-1091
- DOI:
- Cite (ACL):
- Shraey Bhatia, Jey Han Lau, and Timothy Baldwin. 2016. Automatic Labelling of Topics with Neural Embeddings. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 953–963, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Automatic Labelling of Topics with Neural Embeddings (Bhatia et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/C16-1091.pdf