Using Active Learning to Improve Distantly Supervised Entity Typing in Multi-source Knowledge Bases

Abstract

Entity typing in the knowledge base is an essential task for constructing a knowledge base. Previous models mainly rely on manually annotated data or distant supervision. However, human annotation is expensive and distantly supervised data suffers from label noise problem. In addition, it suffers from semantic heterogeneity problem in the multi-source knowledge base. To address these issues, we propose to use an active learning method to improve distantly supervised entity typing in the multi-source knowledge base, which aims to combine the benefits of human annotation for difficult instances with the coverage of a large distantly supervised data. However, existing active learning criteria do not consider the label noise and semantic heterogeneity problems, resulting in much of annotation effort wasted on useless instances. In this paper, we develop a novel active learning pipeline framework to tackle the most difficult instances. Specifically, we first propose a noise reduction method to re-annotate the most difficult instances in distantly supervised data. Then we propose a data augmentation method to annotate the most difficult instances in unlabeled data. We propose two novel selection criteria to find the most difficult instances in different phases, respectively. Moreover, we propose a hybrid annotation strategy to reduce human labeling effort. Experimental results show the effectiveness of our method.