## 1 Introduction
### Related Works  
So far, several authors have worked on diverse research lines related to query expansion, the improvement of efficiency and performance being the common aim, in order to offer more relevant information to the user and better fulfill their needs. [PERSON] et al. [2] evaluated the use of auxiliary collections to address polysemy, synonymy, and hyponymy in clinical text retrieval. These semantic relations complicate the retrieval process as different words can relate to the same topic. In order to deal with this problem, they proposed a pseudo-relevance feedback method that looks for new terms in the auxiliary collections in order to expand the initial query. The authors concluded that the use of all available data, in some cases, is inadequate and may not lead to improvements in the recovery system. In these cases, the authors suggested additional resources and a selection of the collection that is suitable for the query. [PERSON] et al. [4] proposed the Normalized Difference Measure metric, a measure that takes into account the relative frequency of documents and terms in order to improve text classification. This metric analyzes all the terms found in the documents and benefits from the inclusion of new relevant terms in a query when used as a classifier in information retrieval systems. [PERSON] et al. [5] implemented a pseudo-feedback query expansion method that allows the user to select expansion words from a list of possible relevant terms. The authors used the top three retrieved documents to extract terms based on document and word frequencies, word length, and query length. The obtained results showed an overall improvement in the number of relevant retrieved documents, despite the fact that the results in some cases were not better than the base case due to the low number of relevant documents. [PERSON] et al. [6] suggested a combination of several query expansion methods such as Ontologies, Association Rules, WordNet, Methathesaurus, Synonym Mapping, Local Co-occurrence, and Latent Semantic Indexing. The authors noted several limitations to the previous techniques (e.g., performance reduction, term relationship loss), emphasizing the importance of choosing the right technique for each specific case. [PERSON] et al. [7] reviewed multiple query expansion techniques that had been applied to information retrieval systems used in clinical trials. The authors concluded that it is not possible to build an expansion technique pattern that correctly applies to a huge text corpus. They reported that the use of weighted keyword expansion and query reduction (removal of words that are not relevant) improved the performance of information retrieval in clinical trials. [PERSON] et al. [8] proposed a supervised query expansion model that could be applied to highly diverse biomedical datasets. The authors performed a term extraction for each query, proceeded to assign labels to each term, and then ranked them to know which were the most relevant. Owing to these three steps, and with the use of rank weights, the authors were able to enhance the queries and improve the performance of biomedical information retrieval. [PERSON] and [PERSON] [1] surveyed multiple query expansion techniques, weighting methods, and ranking methodologies for information retrieval. They found that the most frequent queries are composed of one, two, or three words, which increases its ambiguity and makes the retrieval process difficult. This exposed the increasing need for query expansion techniques to enhance the original queries with the use of relevant terms, making it easier for information retrieval systems to obtain more suitable elements. [PERSON] et al. [3] proposed a technique that extends deep learning architectures where queries and documents are analyzed together in order to obtain their similarities. The authors claimed that even state-of-the-art complex architectures do not improve the performance of classic algorithms such as BM25 and BM25+extra.