Entangled Relations: Leveraging NLI and Meta-analysis to Enhance Biomedical Relation Extraction
<|reference_start|>Entangled Relations: Leveraging NLI and Meta-analysis to Enhance Biomedical Relation Extraction: 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.<|reference_end|>
@article{hogan2024entangled,
    title={Entangled Relations: Leveraging NLI and Meta-analysis to Enhance
  Biomedical Relation Extraction},
    author={William Hogan and Jingbo Shang},
    journal={arXiv preprint arXiv:2406.00226},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.00226},
    primaryClass={cs.CL}
}