Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach

TL;DR

This paper introduces REHession, a novel relation extraction framework that leverages heterogeneous supervision from knowledge bases and heuristics, using embedding techniques to infer true labels amidst noisy annotations.

Contribution

The paper proposes a new relation extraction method that effectively combines heterogeneous supervision sources with embedding-based label inference, improving over existing approaches.

Findings

REHession outperforms state-of-the-art methods in relation extraction tasks.

Embedding techniques effectively bridge different supervision sources and context information.

Iterative mutual enhancement improves true label discovery from noisy annotations.

Abstract

Relation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-consuming. To overcome this drawback, we propose a novel framework, REHession, to conduct relation extractor learning using annotations from heterogeneous information source, e.g., knowledge base and domain heuristics. These annotations, referred as heterogeneous supervision, often conflict with each other, which brings a new challenge to the original relation extraction task: how to infer the true label from noisy labels for a given instance. Identifying context information as the backbone of both relation extraction and true label discovery, we adopt embedding techniques to learn the distributed representations of context, which bridges all components with mutual enhancement in an iterative fashion. Extensive experimental results demonstrate the superiority of REHession over the state-of-the-art.