Improving Neural Relation Extraction with Positive and Unlabeled Learning

TL;DR

This paper introduces a novel PU learning approach with reinforcement learning for neural relation extraction, effectively utilizing unlabeled data to improve accuracy over existing methods.

Contribution

It proposes a new method combining reinforcement learning and bag representations to leverage unlabeled data in relation extraction.

Findings

Achieves significant improvements over baseline methods

Effectively utilizes unlabeled instances for better performance

Demonstrates robustness on real-world datasets

Abstract

We present a novel approach to improve the performance of distant supervision relation extraction with Positive and Unlabeled (PU) Learning. This approach first applies reinforcement learning to decide whether a sentence is positive to a given relation, and then positive and unlabeled bags are constructed. In contrast to most previous studies, which mainly use selected positive instances only, we make full use of unlabeled instances and propose two new representations for positive and unlabeled bags. These two representations are then combined in an appropriate way to make bag-level prediction. Experimental results on a widely used real-world dataset demonstrate that this new approach indeed achieves significant and consistent improvements as compared to several competitive baselines.