A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction

TL;DR

This paper introduces a data-driven method that enhances biomedical relation extraction from noisy distantly supervised data by extending BERT with multiple instance learning and a novel encoding scheme, achieving state-of-the-art results.

Contribution

It proposes a new approach combining entity-enriched BERT, multiple instance learning, and a simple encoding scheme to effectively reduce noise in distantly supervised biomedical relation extraction.

Findings

Achieved state-of-the-art performance on biomedical relation extraction tasks.

Significantly reduced noise in distantly supervised data.

Encoded relation directionality to improve learning focus.

Abstract

Fact triples are a common form of structured knowledge used within the biomedical domain. As the amount of unstructured scientific texts continues to grow, manual annotation of these texts for the task of relation extraction becomes increasingly expensive. Distant supervision offers a viable approach to combat this by quickly producing large amounts of labeled, but considerably noisy, data. We aim to reduce such noise by extending an entity-enriched relation classification BERT model to the problem of multiple instance learning, and defining a simple data encoding scheme that significantly reduces noise, reaching state-of-the-art performance for distantly-supervised biomedical relation extraction. Our approach further encodes knowledge about the direction of relation triples, allowing for increased focus on relation learning by reducing noise and alleviating the need for joint learning with knowledge graph completion.