A Clustering-Oriented Method for Open-Domain Named Entity Recognition

Jiahui Li, Diange Zhou, Yilin Duan, Xinchuan Li, Hong Yao

Abstract: Named Entity Recognition (NER), as a fundamental task in natural language understanding, has garnered widespread attention. However, most existing research assumes that labeled data is available, limiting the ability of NER models to perform in open-domain scenarios. Especially in real-world applications, the absence of labeled data for novel entity classes is an inevitable scenario. To address this issue, this paper proposes CONER, a Clustering-Oriented Named Entity Recognition method, which mainly consists of two modules: Label Center Clustering (LCC) and Pseudo Label Learning (PLL). LCC uses label classes as clustering centers to guide the learning process of entity representations, thereby achieving high cohesion of entities of the same type in the embedding space. PLL generates pseudo labels based on the cluster-friendly embeddings generated by LCC and uses pairwise similarity learning for the discriminative representation of the novel classes. To balance the learning pace for both seen classes and novel classes, LCC is employed as a pre-training procedure to initialize the model, and then LCC is jointly optimized with PLL. The experimental results on OntoNotes and AnatEM datasets demonstrate that the proposed model outperforms the current zero-shot NER models in open-domain NER, which validates the effectiveness of the approach presented in this paper in open-domain scenarios.