UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining

Jiacheng Li, Jingbo Shang, Julian McAuley


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.426.pdf
Software:
 2022.acl-long.426.software.zip
Code
 JiachengLi1995/UCTopic
Data
BC5CDRCoNLL 2003KP20kKPTimesWNUT 2017