Entangled Relations: Leveraging NLI and Meta-analysis to Enhance Biomedical Relation Extraction

TL;DR

This paper introduces MetaEntailRE, a novel NLI-based method for biomedical relation extraction that significantly improves performance by using meta-class analysis, hypothesis filtering, and group-based prediction, achieving large F1 gains.

Contribution

The paper presents MetaEntailRE, a new adaptation of NLI for relation extraction that incorporates meta-class analysis, hypothesis filtering, and group-based prediction to enhance accuracy.

Findings

F1 gains of 17.6 points on BioRED

F1 gains of 13.4 points on ReTACRED

Significant improvements over traditional RE methods

Abstract

Recent research efforts have explored the potential of leveraging natural language inference (NLI) techniques to enhance relation extraction (RE). In this vein, we introduce MetaEntailRE, a novel adaptation method that harnesses NLI principles to enhance RE performance. Our approach follows past works by verbalizing relation classes into class-indicative hypotheses, aligning a traditionally multi-class classification task to one of textual entailment. We introduce three key enhancements: (1) Meta-class analysis which, instead of labeling non-entailed premise-hypothesis pairs with the less informative "neutral" entailment label, provides additional context by analyzing overarching meta-relationships between classes; (2) Feasible hypothesis filtering, which removes unlikely hypotheses from consideration based on domain knowledge derived from data; and (3) Group-based prediction selection, which further improves performance by selecting highly confident predictions. MetaEntailRE is conceptually simple and empirically powerful, yielding significant improvements over conventional relation extraction techniques and other NLI formulations. We observe surprisingly large F1 gains of 17.6 points on BioRED and 13.4 points on ReTACRED compared to conventional methods, underscoring the versatility of MetaEntailRE across both biomedical and general domains.