The automatic extraction of biomedical entities and their interaction from
unstructured data remains a challenging task due to the limited availability of
expert-labeled standard datasets. In this paper, we introduce PETAI-LOR, a
retrieval-based language framework that is augmented by tailored chunk scorer.
Unlike previous retrieval-augmented language models (LM) that retrieve relevant
documents by calculating the similarity between the input sentence and the
candidate document set, PETAILOR segments the sentence into chunks and
retrieves the relevant chunk from our pre-computed chunk-based relational
key-value memory. Moreover, in order to comprehend the specific requirements of
the LM, PETAI-LOR adapt the tailored chunk scorer to the LM. We also introduce
GM-CIHT, an expert annotated biomedical triple extraction dataset with more
relation types. This dataset is centered on the non-drug treatment and general
biomedical domain. Additionally, we investigate the efficacy of triple
extraction models trained on general domains when applied to the biomedical
domain. Our experiments reveal that PETAI-LOR achieves state-of-the-art
performance on GM-CIHT

本研究提出了一个增强型的基于句子切块和关系记忆的检索增强语言模型 PETAI-LOR，针对生物医学实体和其相互作用的自动提取问题，在专门设计的数据集上获得了最新的性能结果。