## 2 Materials and Methods
### Text Preprocessing and Matching  
The information retrieval process involves data preprocessing and matching. The _preprocessing_ step includes tasks related to tokenization, stopword removal, sentence detection, stemming, lemmatization, and term weighting. Tokenization allows for the transformation of a document into words using white spaces, commas, periods, and tab delimiters as separators during the token-building process. According to [10], there are different text delimiters that can lead to a complex process of tokenization. Since most scientific documents are written in English, the recognition and extraction of tokens is carried out considering a specific set of characters. A whole set of special characters is disregarded as they contribute nothing to the knowledge and only function as token separators. Among them are: \(()\), \(/\), \(\{\), \(\}\), \(\{\}\), \(\}\); \(\vdots\)  
Figure 1: Information retrieval—general process. There are some issues that need to be taken into account, such as the identification of abbreviations, dates, acronyms, and letter capitalization. Case transformation allows for the standardization of the words contained in documents, thus dropping different versions of the same word. Given that the stopword list is presented in lowercase, all the letters are transformed to its lowercase variant. Stopword removal is based on the elimination and non-consideration of words that are very frequent and offer little significance. The main advantage of this procedure is the reduction of data size, and thus the decrease of computational cost and the improvement of accuracy. There are lists of stopwords available for the English language. However, new terms may be added to these lists depending on the structure of the documents and the needs of particular circumstances. Stemming is a process of reducing words to their word stem, preserving only the morphological root. Suffixes of words such as plurals and gerunds, among others, are removed. According to the literature, _Porter Stemmer_ and _Krovetz Stemmer_ are the most frequently used stemmers in information retrieval systems in the English corpora. Porter Stemmer was developed by [PERSON] at Cambridge University in 1980 and was first published in [PERSON], M.F [11]. It is a process of removing word suffixes, such as gerunds and plurals, and replacing inflectional endings. It consists of rules dealing with a specific suffixes and according to certain conditions. Lemmatization uses dictionaries and a morphological analysis of words in order to reflect the base form of a word, consequently collapsing the inflectional forms. Document indexing is based on the frequency of the words that each document contains. Words with a high number of repetitions have a higher frequency, while the others have a lower frequency [12]. This is not always desirable behavior as some words such as and, the, and or appear frequently in documents but do not offer relevant information. One of the most widely applied algorithms is Term Frequency-Inverse Document Frequency (TF-IDF) [13], a statistical measure that assesses the importance of a word for a document in a collection or corpus. The TF-IDF value increases proportionally to the number of times a word appears in the document, but is offset by the frequency of the word in the document collection, which recognizes the fact that some words are generally more common than others. The matching step allows for the calculation of the similarity between documents and queries, with an associated weighting of terms. In general, a retrieval system returns a list of ordered documents where the first is the document most similar to the query. Taking this into account, it is possible to reformulate the query and expand it to be more representative of the need of the user; this technique is known as query expansion. According to the literature, query expansion techniques can be classified as: query-specific, corpus-specific, or language-specific [14].