Related papers: Actively Supervised Clustering for Open Relation Extraction

Actively Supervised Clustering for Open Relation Extraction

URL: http://arxiv.org/abs/2306.04968v1
Date: Thu, 8 Jun 2023 06:55:02 GMT
Title: Actively Supervised Clustering for Open Relation Extraction
Authors: Jun Zhao, Yongxin Zhang, Qi Zhang, Tao Gui, Zhongyu Wei, Minlong Peng, Mingming Sun
Abstract summary: We present a novel setting, named actively supervised clustering for OpenRE. The key to the setting is selecting which instances to label. We propose a new strategy, which is applicable to dynamically discover clusters of unknown relations.
Score: 42.114747195195655
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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