# Document Retrieval System for Biomedical Question Answering
###### Abstract  
In this paper, we describe our biomedical document retrieval system and answers extraction module, which is part of the biomedical question answering system. Approximately 26.5 million PubMed articles are indexed as a corpus with the Apache Lucene text search engine. Our proposed system consists of three parts. The first part is the question analysis module, which analyzes the question and enriches it with biomedical concepts related to its wording. The second part of the system is the document retrieval module. In this step, the proposed system is tested using different information retrieval models, like the Vector Space Model, Okapi BM25, and Query Likelihood. The third part is the document re-ranking module, which is responsible for re-arranging the documents retrieved in the previous step. For this study, we tested our proposed system with 6B training questions from the BioASQ challenge task. We obtained the best MAP score on the document retrieval phase when we used Query Likelihood with the Dirichlet Smoothing model. We used the sequential dependence model at the re-rank phase, but this model produced a worse MAP score than the previous phase. In similarity calculation, we included the Named Entity Recognition (NER), UMLS Concept Unique Identifiers (CUI), and UMLS Semantic Types of the words in the question to find the sentences containing the answer. Using this approach, we observed a performance enhancement of roughly 25% for the top 20 outcomes, surpassing another method employed in this study, which relies solely on textual similarity. information retrieval; document retrieval; biomedical question answering; search engine; natural language processing 2024  
to open domain QA systems, far fewer researchers are working on the medical branch of domain-specific question answering. [PERSON] and his colleagues [2] have customized an open-domain QA system to construct answers for questions related to the genome, focusing on defining term relations based on a linguistic-rich full-parser. Due to the continuous increase in information produced in the biomedical field, there is also an increasing need for biomedical QA, especially for the public, medical students, healthcare professionals, and biomedical researchers [3]. Biomedical QA is one of the most important applications of real-world biomedical systems, and there are many initiatives to promote research in this field [1]. The BioASQ challenge is an organization that has been focusing on the advancement of contemporary biomedical semantic indexing and QA at large-scale since 2013 [4]. The organizers have undertaken the task of evaluating existing solutions to various QA sub-tasks. Within this organization, various benchmarks have been provided to assess researchers in the field of QA systems [5]. The BioASQ challenge consists of two tasks: large-scale biomedical semantic indexing and QA. Several systems have been developed within the scope of this challenge. To begin, the NCBI framework [6] is one of these. It uses a PubMed search to return relevant documents. It employs cosine similarity to calculate the likeness between the question and sentence. The highest-scored sentence in the abstract is to be returned as a snippet. A modified dictionary search algorithm is being used with MetaMap [7] to identify biomedical concepts. BioPortal services are also employed to expand the query. For example, for the question: \"Is Rheumatd Arthritis more common in men or women?\" the synonyms extracted are \"arthropathy\" for \"arthritis\" and \"female\", \"femme\", and \"adult\" for \"women\". The system presented in [8] depends on the SAP HANA Database, which has text analysis features, like tokenization, sentence splitting, named-entity recognition, full-text indexing, and approximate text matching, for text processing. Another example is the CMU OAQA system, which has a hierarchical retrieval architecture. In this system, each query is completed in three steps. In the first step, all stop words are removed from the query.