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 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 cluster-ing tasks. Comprehensive evaluation on topic mining shows that UCTopic can extract coherent and diverse topical phrases.

本文提出了UCTopic，一种使用无监督对比学习框架来学习上下文感知短语表示和进行主题挖掘，通过正对构建来预训练UCTopic，使用聚类辅助对比学习(CCL)显着降低噪声负例，进一步提高了短语表示的类别学习效果，在实体聚类任务上优于现有的同类模型。

UCTopic：无监督对比学习短语表征与主题挖掘