Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric Bayesian Perspective

Qing Zhang, Houfeng Wang

Abstract:

For the task of relation extraction, distant supervision is an efficient approach to generate labeled data by aligning knowledge base with free texts. The essence of it is a challenging incomplete multi-label
classification problem with sparse and noisy features. To address the challenge, this work presents a novel nonparametric Bayesian formulation for the task. Experiment results show substantially higher top-precision improvements over the traditional state-of-the-art approaches.