Extraction of causal relations between biomedical entities in the form of Biological Expression Language (BEL) poses a new challenge to the community of biomedical text mining due to the complexity of BEL statements. We propose a simplified form of BEL statements [Simplified Biological Expression Language (SBEL)] to facilitate BEL extraction and employ BERT (Bidirectional Encoder Representation from Transformers) to improve the performance of causal relation extraction (RE). On the one hand, BEL statement extraction is transformed into the extraction of an intermediate form—SBEL statement, which is then further decomposed into two subtasks: entity RE and entity function detection. On the other hand, we use a powerful pretrained BERT model to both extract entity relations and detect entity functions, aiming to improve the performance of two subtasks. Entity relations and functions are then combined into SBEL statements and finally merged into BEL statements. Experimental results on the BioCreative-V Track 4 corpus demonstrate that our method achieves the state-of-the-art performance in BEL statement extraction with F1 scores of 54.8% in Stage 2 evaluation and of 30.1% in Stage 1 evaluation, respectively. Database URL: https://github.com/grapeff/SBEL_datasets

中文翻译：

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基于SBEL和BERT模型的因果关系提取

由于 BEL 语句的复杂性，以生物表达语言 (BEL) 形式提取生物医学实体之间的因果关系对生物医学文本挖掘社区提出了新的挑战。我们提出了一种简化形式的 BEL 语句 [Simplified Biological Expression Language (SBEL)] 以促进 BEL 提取，并采用 BERT（来自 Transformers 的双向编码器表示）来提高因果关系提取 (RE) 的性能。一方面，BEL语句抽取转化为中间形式的抽取——SBEL语句，然后进一步分解为实体RE和实体函数检测两个子任务。另一方面，我们使用强大的预训练 BERT 模型来提取实体关系和检测实体函数，旨在提高两个子任务的性能。然后将实体关系和函数组合成 SBEL 语句，最后合并成 BEL 语句。BioCreative-V Track 4 语料库的实验结果表明，我们的方法在 BEL 语句提取中实现了最先进的性能，在第 2 阶段评估中的 F1 分数分别为 54.8% 和在第 1 阶段评估中的 30.1%。数据库网址：https://github.com/grapeff/SBEL_datasets