Biomedical Relation Extraction with Entity Type Markers and Relation-specific Question Answering (2023)

Abstract: Recently, several methods have tackled the relation extraction task with QA and have shown successful results. However, the effectiveness of existing methods in specific domains, such as the biomedical domain, is yet to be verified. When there are multiple entity pairs that share an entity in a sentence, a QA-based relation extraction model that outputs only one single answer to a given question may not extract desired relations. In addition, these methods employ QA models that are not tuned for relation extraction. To address these issues, we first extend and apply a span QA-based relation extraction method to the drug-protein relation extraction by creating question templates and incorporating entity type markers. We further propose a binary QA-based method that directly uses the entity information available in the relation extraction task. The experimental results on the DrugProt dataset show that our QA-based methods, especially the proposed binary QA method, are effective for drug-protein relation extraction.

## introduction in recent years, several methods that tackle the relation extraction (re) task with question answering (qa) have shown impressive results @xcite @xcite . instead of directly finding a relation between an entity pair in a sentence, in these methods, questions relating to the relation between the entity pair are answered by a qa model to determine whether the entity pair has the relation. for example, suppose that we have the following sentence "tom was born in 1999". to determine whether the relation born_in exists or not between the entity pair @xcite builds two questions, each containing one of the two entities, e.g., "when was tom born?" for an entity tom and "who was born in 1999?" for an entity 1999. if the answer from a span qa model is the other entity that is not in the question for one of these questions, the method determines that there is a relation "born_in" between the pair. such a qa-based re method can focus on each relation by preparing different questions for different relations. however, existing methods have been applied only to general domain datasets, and their effectiveness has not been verified in specific domains, such as the biomedical domain. when there are multiple entity pairs that share an entity in a sentence, a qa model that can output only one answer to a question cannot extract relations since the questions become identical for the same entity. for example, when using a question "who was born in 1999?" to determine if there is a relationship born_in between the entity pair ("tom", "1999") and ("john", "1999") in the sentence "john was born in 1999, like tom.," the answer will be either tom or john because the input to the model is the same. in this case, it is determined that there is no relation "born_in" for one of the entity pairs. moreover, the employed qa models are not tuned for re. in this study, we extend and apply a span qabased re method @xcite for drugprotein re to check the effectiveness of the method in the biomedical domain task. for this purpose, we manually create question templates for a drugprotein re dataset drugprot @xcite . to deal with each entity pair, even if multiple entity pairs share the same entity, we introduce entity type markers that incorporate entity types available in the re task into the question and input sentence. furthermore, we propose a binary qabased method that directly classifies whether the answer to a question, which includes one entity of the target entity pair, is the other entity of the pair or not. our contributions are summarized as follows: qa-based methods have also been applied to other tasks, such as named entity recognition @xcite , event extraction @xcite , and coreference resolution @xcite . in the biomedical domain, the effectiveness of qa-based methods has been verified in event extraction @xcite and spatial information extraction @xcite . ## method this section describes our re models that employ the span qa-based re following @xcite to drug-protein re and proposes a binary qa-based method. we first introduce the question templates to apply qa-based re to drug-protein re in section 3.1. we next introduce entity type markers, which incorporate the information about the entity types into the input sentence and question in section 3.2. we then explain a span qa-based method that predicts the start and end of the answer span in section 3.3. we finally propose a binary qa-based method in section 3.4. ## conclusions in this paper, we applied and extended span qabased re method to the drug-protein re by creating question templates and incorporating entity type markers. we also proposed the binary qabased method that directly uses the entity information available in the re task. we showed the qa-based methods, especially the proposed binary qa method, are effective for drug-protein re. for future work, we will analyze the advantages and problems of the proposed qa-based methods by comparing them with other re methods in the biomedical domain. in addition, we will analyze difficulties in the biomedical domain by applying and comparing qa-based methods in the general domain.