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

• URL: http://arxiv.org/abs/2406.00226v1
• Date: Fri, 31 May 2024 23:05:04 GMT
• Title: Entangled Relations: Leveraging NLI and Meta-analysis to Enhance Biomedical Relation Extraction
• Authors: William Hogan, Jingbo Shang,
• Abstract summary: We introduce MetaEntail-RE, a novel adaptation method that harnesses NLI principles to enhance relation extraction. Our approach follows past works by verbalizing relation classes into class-indicative hypotheses. Our experimental results underscore the versatility of MetaEntail-RE, demonstrating performance gains across both biomedical and general domains.
• Score: 35.320291731292286
• License: http://creativecommons.org/licenses/by/4.0/
• 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 MetaEntail-RE, 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) Instead of labeling non-entailed premise-hypothesis pairs with the uninformative "neutral" entailment label, we introduce meta-class analysis, which provides additional context by analyzing overarching meta relationships between classes when assigning entailment labels; (2) Feasible hypothesis filtering, which removes unlikely hypotheses from consideration based on pairs of entity types; and (3) Group-based prediction selection, which further improves performance by selecting highly confident predictions. MetaEntail-RE is conceptually simple and empirically powerful, yielding significant improvements over conventional relation extraction techniques and other NLI formulations. Our experimental results underscore the versatility of MetaEntail-RE, demonstrating performance gains across both biomedical and general domains.

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