Improving researcher's area of expertise identification using TF-IDF Characters N-grams
ResumoAs the academic information on the internet became broadly available in the shape of academic social networks and academic profiles, its usage to help to resolve tasks like the discovery of specialists in a given area, identification of potential scholarship holders, or suggestion of collaborators, for example, had a growth in importance and relevance. In the case of academic social networks, the Brazilian government created the Lattes Platform in order to manage academic data from Brazilian researchers as well as use it to help in the evaluation of researchers and groups of researchers. However, in order to use the Lattes Platform information to help in the aforementioned tasks, it is important to check the quality of the data, because most of it is declared by the users and does not have any verification of its veracity, specially regarding the declared main expertise area. Thus, this article explores the usage of machine learning techniques to recognize the main areas of expertise of researchers using several numerical representations to represent its scientific production titles as data source for the algorithms. We have been able to surpass the current state-of-art results to resolve this problem by using a TF-IDF character n-gram representation for the text in the titles, achieving an accuracy of 95.91%.
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