为生物关系抽取添加方向性标注，提升模型对复杂网络的建模能力。
Enhancing Biomedical Relation Extraction with Directionality
- 基于软提示学习设计多任务模型，同时识别关系、新发现和实体角色。
- 构建含10,864条方向性标注的增强版BioRED数据集，提升下游任务性能。
- 在两个基准任务上超越GPT-4和Llama-3，适合生物医学知识图谱研究者。
生物关系网络蕴含基因、蛋白、疾病和化学物质等实体间关系的丰富信息，有助于理解其生物学机制。随着生物医学文献的快速增长，更新网络知识面临挑战。最近发布的生物医学关系抽取数据集（BioRED）提供了人工标注，推动了机器学习与预训练语言模型在文档级（跨句上下文）关系自动识别方面的发展。然而，其标注缺乏实体角色的方向性（主语/宾语），这对研究复杂生物网络至关重要。本文对BioRED语料库中的关系实体角色进行了标注，并提出一种新型多任务语言模型，结合软提示学习，联合识别关系、新发现及实体角色。结果包括一个包含10,864条方向性标注的增强版BioRED语料库。此外，所提方法在两个基准任务上表现优于现有大模型，如最先进的GPT-4和Llama-3。源代码与数据集已公开于https://github.com/ncbi-nlp/BioREDirect。
原文摘要 · Abstract (English)
Biological relation networks contain rich information for understanding the biological mechanisms behind the relationship of entities such as genes, proteins, diseases, and chemicals. The vast growth of biomedical literature poses significant challenges updating the network knowledge. The recent Biomedical Relation Extraction Dataset (BioRED) provides valuable manual annotations, facilitating the develop-ment of machine-learning and pre-trained language model approaches for automatically identifying novel document-level (inter-sentence context) relationships. Nonetheless, its annotations lack directionality (subject/object) for the entity roles, essential for studying complex biological networks. Herein we annotate the entity roles of the relationships in the BioRED corpus and subsequently propose a novel multi-task language model with soft-prompt learning to jointly identify the relationship, novel findings, and entity roles. Our results in-clude an enriched BioRED corpus with 10,864 directionality annotations. Moreover, our proposed method outperforms existing large language models such as the state-of-the-art GPT-4 and Llama-3 on two benchmarking tasks. Our source code and dataset are available at https://github.com/ncbi-nlp/BioREDirect.
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