## 4 Conclusions  
In this study, a new unsupervised query expansion technique using Multinomial Naive Bayes was presented. An expanded query was obtained by the combination of terms found in the original query and the new terms retrieved by the Multinomial Naive Bayes method. The extraction of the vocabulary from the documents retrieved by the combined query, and the selection of terms that were likely to belong to a category both proved to be effective in recovering more relevant documents.  
More specifically, the application of this Pseudo-Feedback technique proved to be satisfactory considering the MAP and Precision results. The first 5, 10, 15, and 100 documents retrieved were considered in the evaluation process. Even when the first 100 documents  
Figure 8: Comparison of the mean precision of the participants of the TREC2017 Precision Medicine Track (TREC) and the proposed method (EQ) in the top 15 retrieved documents.  
Figure 9: Mean improvement of the best teams at the TREC2017 Precision Medicine Track (TREC) over their respective previous team as compared to the improvement of the proposed method (EQ) over the best team results.  
were taken into account, the results improved, which shows that it is possible to increase their quantity and continue to improve the retrieval process quality.  
The proposed query expansion technique allows for the improvement of a lightly defined query made by the user in order to obtain better results. This will help users to find relevant documents that fulfill their needs, and easily filter documents found in large and specialized collections of documents, such as the medical corpora, where technical lexicon vocabulary make it difficult to find relevant content through a short query composed of keywords.  
Inspired by the success of this new method, more techniques could be researched. We plan to review some topic modeling techniques that could offer more terms inspired in the topic covered by the first retrieved documents, which would allow for a more complex and wider query expansion. In addition, new ways of combining the terms found in the original query and the expansion terms are being studied.  
Conceptualization, S.S.; methodology, S.S.; software, S.S.; validation, L.B., E.L.I. and A.S.V.; formal analysis, S.S.; investigation, S.S. and A.S.V.; resources, S.S.; data curation, S.S. and A.S.V.; writing--original draft preparation, S.S. and P.C.; writing--review and editing, S.S., P.C. and L.B.; visualization, L.B.; supervision, E.L.I. and L.B.; project administration, E.L.I. and L.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The Clinical Trial corpus used in this work is available on the TREC official web page [[http://www.trec-cds.org/2017.html](http://www.trec-cds.org/2017.html)]([http://www.trec-cds.org/2017.html](http://www.trec-cds.org/2017.html)).  
The SING group thanks CITI (Centro de Investigacion, Transferencia e Innovacion) from the University of Vigo for hosting its IT infrastructure. We also appreciate the support provided by Conselleria de Educacion, Universidades e Formacion Professional (Xunta de Galicia) under the scope of the strategic funding of ED43 IC2018/55-GRC Competitive Reference Group. [PERSON] is supported by a pre-doctoral fellowship from Xunta de Galicia (ED481A 2021/286). The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript.  
The authors declare no conflicts of interest.