2023/06/08 by Jun Zhao, Yongxin Zhang, Zhao, Jun +11
Computer Science · Decision Sciences · #Advanced Clustering Algorithms Research #Computation and Language (cs.CL) #Data Mining Algorithms and Applications #Data Quality and Management #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2306.04968
openalex publication_date 2023/06/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Current clustering-based Open Relation Extraction (OpenRE) methods usually adopt a two-stage pipeline. The first stage simultaneously learns relation representations and assignments. The second stage manually labels several instances and thus names the relation for each cluster. However, unsupervised objectives struggle to optimize the model to derive accurate clustering assignments, and the number of clusters has to be supplied in advance. In this paper, we present a novel setting, named actively supervised clustering for OpenRE. Our insight lies in that clustering learning and relation labeling can be alternately performed, providing the necessary guidance for clustering without a significant increase in human effort. The key to the setting is selecting which instances to label. Instead of using classical active labeling strategies designed for fixed known classes, we propose a new strategy, which is applicable to dynamically discover clusters of unknown relations. Experimental results show that our method is able to discover almost all relational clusters in the data and improve the SOTA methods by 10.3% and 5.2%, on two datasets respectively.