Biomedical Question Answering: A Survey of Approaches and Challenges
Automatic Question Answering (QA) has been successfully applied in various domains such as search engines and chatbots. Biomedical QA (BQA), as an emerging QA task, enables innovative applications to effectively perceive, access and understand complex biomedical knowledge. There have been tremendous developments of BQA in the past two decades, which we classify into 5 distinctive approaches: classic, information retrieval, machine reading comprehension, knowledge base and question entailment approaches. In this survey, we introduce available datasets and representative methods of each BQA approach in detail. Despite the developments, BQA systems are still immature and rarely used in real-life settings. We identify and characterize several key challenges in BQA that might lead to this issue, and discuss some potential future directions to explore.
Code (0)
등록된 구현이 없습니다.
Tasks
Information RetrievalMachine Reading ComprehensionQuestion AnsweringReading ComprehensionRetrievalSurveySimilar Papers 제목 키워드 기반
Question Answering in the Biomedical Domain
Question answering techniques have mainly been investigated in open domains. However, there are particular challenges in extending these open-domain techniques to extend into the biomedical domain. Question answering foc…
Question AnsweringTop K Relevant Passage Retrieval for Biomedical Question Answering
Question answering is a task that answers factoid questions using a large collection of documents. It aims to provide precise answers in response to the user's questions in natural language. Question answering relies on …
ArticlesPassage RetrievalQuestion AnsweringReading Comprehension+1Overview of TREC 2024 Biomedical Generative Retrieval (BioGen) Track
With the advancement of large language models (LLMs), the biomedical domain has seen significant progress and improvement in multiple tasks such as biomedical question answering, lay language summarization of the biomedi…
Medical Question AnsweringQuestion AnsweringRetrievalBiomedical Multi-hop Question Answering Using Knowledge Graph Embeddings and Language Models
Biomedical knowledge graphs (KG) are heterogenous networks consisting of biological entities as nodes and relations between them as edges. These entities and relations are extracted from millions of research papers and u…
Knowledge Graph EmbeddingsKnowledge GraphsMulti-hop Question AnsweringQuestion AnsweringAI for Biomedicine in the Era of Large Language Models
The capabilities of AI for biomedicine span a wide spectrum, from the atomic level, where it solves partial differential equations for quantum systems, to the molecular level, predicting chemical or protein structures, a…
Language ModelingLanguage ModellingLarge Language ModelTime Series