Abstract
High-quality phrase representations are essential to finding topics and related terms in documents (a.k.a. topic mining). Existing phrase representation learning methods either simply combine unigram representations in a context-free manner or rely on extensive annotations to learn context-aware knowledge. In this paper, we propose UCTopic, a novel unsupervised contrastive learning framework for context-aware phrase representations and topic mining. UCTopic is pretrained in a large scale to distinguish if the contexts of two phrase mentions have the same semantics. The key to the pretraining is positive pair construction from our phrase-oriented assumptions. However, we find traditional in-batch negatives cause performance decay when finetuning on a dataset with small topic numbers. Hence, we propose cluster-assisted contrastive learning (CCL) which largely reduces noisy negatives by selecting negatives from clusters and further improves phrase representations for topics accordingly. UCTopic outperforms the state-of-the-art phrase representation model by 38.2% NMI in average on four entity clustering tasks. Comprehensive evaluation on topic mining shows that UCTopic can extract coherent and diverse topical phrases.- Anthology ID:
- 2022.acl-long.426
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6159–6169
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.426
- DOI:
- 10.18653/v1/2022.acl-long.426
- Cite (ACL):
- Jiacheng Li, Jingbo Shang, and Julian McAuley. 2022. UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6159–6169, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining (Li et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2022.acl-long.426.pdf
- Code
- JiachengLi1995/UCTopic
- Data
- BC5CDR, CoNLL 2003, KP20k, KPTimes, WNUT 2017