Work

Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning

Year: 2018

Type: conference-paper

Abstract: Distant supervision has become the standard method for relation extraction.However, even though it is an efficient method, it does not come at no cost-The resulted distantly-supervised training samples are often very noisy.To combat the noise, most of the recent state-of-theart approaches focus on selecting onebest sentence or calculating soft attention weights over the set of the sentences of one specific entity pair.However, these methods are suboptimal, and the false positive problem is still a key stumbling bottleneck for the performance.We argue that those incorrectly-labeled candidate sentences must be treated with a hard decision, rather than being dealt with soft attention weights.To do this, our paper describes a radical solution-We explore a deep reinforcement learning strategy to generate the false-positive indicator, where we automatically recognize false positives for each relation type without any supervised information.Unlike the removal operation in the previous studies, we redistribute them into the negative examples.The experimental results show that the proposed strategy significantly improves the performance of distant supervision comparing to state-of-the-art systems.

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/p18-1199

Authors: Pengda Qin, Weiran Xu, William Yang Wang

Institutions: Beijing University of Posts and Telecommunications, University of California, Santa Barbara

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities

Awards: G2860619779, G5876998749, G8858434837