Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric Bayesian Perspective
Qing Zhang, Houfeng Wang · 2017
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.