# Document Retrieval System for Biomedical Question Answering
## 2 Materials and Methods
### QA System Components
#### 2.2.2 Document Retrieval  
The task of this component is to find documents containing the answer for the question. We employed the MEDLINE database as the corpus. Documents in MEDLINE contain miscellaneous information, such as journal names, contents of the title, author, abstract, publication date, chemical codes, Medical Subject Heading (MeSH) terms [31], and MeSH codes. After analyzing these files, we selected the fields for title, abstract, publication date, chemical codes, MeSH terms, and MeSH codes to index with the Apache Lucene search engine. Approximately 26.5 million PubMed articles were indexed as a corpus with the Apache Lucene text search engine for this study. For ranking documents, four models were used: Vector Space Model, Okapi BM25, Query Likelihood with Dirichlet Smoothing, and the Jelinek-Mercer Smoothing Model. The search process consisted of four steps. The system performance was measured according to the results of each separate step in the search process. Lucene Step: This is the first step in the search process. Similar documents are retrieved by sending a modified query to the lucene search engine. Stemming Step: This is the second step in the search process. In this step, the modified query is expanded by adding root forms of the modified query words. The new query obtained is sent to the lucene search engine. Feedback Step: This is the third step in the search process. In this step, the system retrieves the documents from the previous step and receives the five most frequently repeated mesh terms in those documents. The new query is obtained by adding these mesh terms to the modified query. The new query obtained is sent to the lucene search engine. Re-rank Step: This is the last step in the search process. The 1000 most relevant documents are retrieved from the previous step. These documents are split into trigram units [15]. A trigram unit is generated by the sliding window technique. In this method, the first trigram unit is generated using the first, second, and third sentences. The second trigram unit is generated using the second, third, and fourth sentences. The third trigram unit is generated by using the third, fourth, and fifth sentences, and so on. This method aims to find the most relevant sentences that might include the answer. Two similarity scores are used to obtain the similarity between the question and documents. These are document similarity and trigram unit similarity scores. These similarity scores are calculated as shown in Equation (7).  
\[score_{re-rank}=\alpha*score_{trigram}+(1-\alpha)*score_{doc} \tag{7}\]  
\(score_{trigram}\) denotes the similarity score of the trigram unit, and \(score_{doc}\) denotes the similarity score of the documents. The weighting parameter \(\alpha\) has a value between 0 and 1. To give more weight to the trigram unit, \(\alpha\) parameter is set as 0.65.  
Figure 1: Question Analysis System Architecture. The modified query was used in the document retrieval phase. We employed 6B training questions from the BioASQ challenge task as the question set of our study.  
According to results obtained from the re-ranking operation, the top-ranked 20 documents are retrieved and returned as an output of the system. Our proposed document retrieval system architecture is shown in Figure 2.