A Question-answering Based Framework for Relation Extraction Validation

Abstract

Relation extraction is an important task in knowledge acquisition and text understanding. Existing works mainly focus on improving relation extraction by extracting effective features or designing reasonable model structures. However, few works have focused on how to validate and correct the results generated by the existing relation extraction models. We argue that validation is an important and promising direction to further improve the performance of relation extraction. In this paper, we explore the possibility of using question answering as validation. Specifically, we propose a novel question-answering based framework to validate the results from relation extraction models. Our proposed framework can be easily applied to existing relation classifiers without any additional information. We conduct extensive experiments on the popular NYT dataset to evaluate the proposed framework, and observe consistent improvements over five strong baselines.

References in corpus (8)

• Relation Classification via Recurrent Neural Network
• Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning
• Reinforcement Learning for Relation Classification from Noisy Data
• Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix
• Attention Guided Graph Convolutional Networks for Relation Extraction
• Relation Extraction : A Survey
• Multi-Level Structured Self-Attentions for Distantly Supervised Relation Extraction
• QA4IE: A Question Answering based Framework for Information Extraction