# Document Retrieval System for Biomedical Question Answering
###### Abstract
The Dirichlet smoothing retrieval model is used for scoring documents, and only the top 10,000 are retrieved. The Negative Query Generation (NQG) model is used to re-rank documents in the second step. After re-ranking, only the top 100 documents are to be retrieved. In the third step, in-depth analysis is performed on documents, and pre-trained Learning to Rank (LETOR) algorithms are used to score documents, and then only the top 10 documents are retrieved [9]. Ref. [10] has built a generic retrieval model based on the Sequential Dependence Model, Word Embedding, and the Ranking Model for document retrieval. In this generic model, titles have special significance, and top-K results are re-ranked according to meaningful nouns in their titles. The [PERSON] team uses a statistical language model and query likelihood model to retrieve relevant documents. They have employed the Indri search engine to build their system. According to this, nouns in the query have higher significance. Retrieved documents are re-ranked according to the presence of the keywords in the query in the document. If the document contains all the keywords in the query, it will score higher than others [11]. The IIIT-Hyderabad team has used chunking, stop words removal, and query formulation techniques to retrieve documents. The most relevant phrases are collected as snippets from the top documents. Cosine similarity and noun chunk identification techniques have been used to produce exact and ideal answers from the snippets [12]. Pubmed articles are indexed by the Lucene search engine. Various models have been employed to retrieve documents in this system. They include cosine similarity, sequential dependence, and semantic concept-enriched dependence models. This system uses UMLS concepts in the query as additional criteria for ranking the documents [13]. Another team [14] has used the Indri search engine for document retrieval. A unigram language model with Dirichlet prior smoothing is used as a retrieval method. Generally, the sequential dependence model (SDM) and semantic concept-enriched dependence model (SCDM) perform better than the method used in the baseline query likelihood model (QL). KSAnswer is a biomedical QA system that returnsthe most relevant documents and snippets. Candidate snippets are retrieved by using a cluster-based language model. Five independent similarity models are used to re-rank the retrieved top-N snippets [15]. The \"AUTH\" team has tried approaches based on BioASQ search services and ElasticSearch for the document retrieval task by querying the top 10 documents, with a combination of words in each question [16]. The AUEB team has developed their BioASQ6 document retrieval system, which they have modified to give a relevance score for each sentence, and experimented with BERT and PACRR for this task [17]. The Google team has used different models in their system, such as the BM25 retrieval model, BioBERT, Synthetic Query Generation (QGen), a retrieval model based on BM25, and a neural model [18]. Additionally, the BERT model was used to re-rank the results [19]. The University of Aveiro's \"bioinfo\" team has created a retrieval process consisting of two stages. Initially, they utilized the traditional BM25 model in the first stage. Subsequently, the second stage incorporated neural re-ranking models based on transformers, specifically sourced from Pub-MedBERT and monoT5 checkpoints [20]. The University of Regensburg's team has employed \"UR-gpt\" systems that harnessed two commercial iterations of the GPT Large Language Model (LLM). Their approach involved experimenting with both GPT-3.5-turbo and GPT-4 models. Zero-shot learning for query expansion, query re-formulation, and re-ranking was utilized in this system [21]. The MindLab team's approach in the realm of document retrieval involved utilizing the BM25 scoring function coupled with a re-ranking strategy based on semantic similarity [22]. Fudan University's \"dmiip\" team has employed a two-stage approach in their systems.