MRC4BioER: Joint extraction of biomedical entities and relations in the machine reading comprehension framework

Extracting entities and their relations from unstructured literature to form structured triplets is essential for biomedical knowledge extraction. Because sentences in biomedical datasets usually have many special overlapping triplets, it is difficult to use previous work to extract these triplets effectively. In this work, we propose a novel tagging strategy to achieve joint extraction in the machine reading comprehension framework. On the one hand, our method uses Query in the machine reading comprehension framework to introduce the information of the specific relation. On the other hand, our method introduces a tagging strategy for overlapping triplets in the biomedical domain. We use CHEMPROT and DDIExtraction2013 datasets to evaluate our method. The experimental results demonstrate that our proposed method can enhance the model's ability to deal with overlapping triplets, improving extraction performance.