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

2024-05-31

arXiv.org

AI Summary

This study addresses key challenges in biomedical relation extraction (RE)—namely, difficulty in fine-grained relation classification and strong interference from neutral (non-relational) instances—by proposing a novel natural language inference (NLI)-based paradigm. Methodologically, it reformulates RE as a multi-hypothesis NLI task, where relation categories are explicitly encoded via semantic entailment. To mitigate class imbalance, it introduces “meta-relation analysis” as a principled alternative to conventional neutral labels. Additionally, it incorporates domain-knowledge-enhanced plausible hypothesis filtering and confidence-ranked group-wise prediction selection. Evaluated on BioRED and ReTACRED benchmarks, the approach achieves absolute F1 improvements of 17.6 and 13.4 percentage points, respectively, outperforming state-of-the-art RE models and existing NLI-RE methods. These results empirically validate the effectiveness of jointly modeling semantic inference and structured meta-analysis for biomedical RE.

Technology Category

• Natural Language Processing: Information Extraction
• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Statistical Relational/Logic Learning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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.

Problem

• Enhance biomedical relation extraction using NLI techniques.
• Introduce MetaEntailRE for improved RE performance via meta-analysis.
• Achieve significant F1 score gains in biomedical and general domains.

Innovation

• Uses NLI for biomedical relation extraction enhancement
• Introduces meta-class analysis for context-rich entailment labeling
• Implements group-based prediction for higher confidence selections