Automatic classifier of scientific contents

Abstract

The growth of scientific production, associated with the increase in the complexity of scientific contents, makes the classification of these contents highly subjective and subject to misinterpretation. The taxonomy on which this classification process is based does not follow the scientific areas' changes. These classification processes are manually carried out and are therefore subject to misclassification. A classification process that allows automation and implements intelligent algorithms based on Machine Learning algorithms presents a possible solution to subjectivity in classification. Although it does not solve the inadequacy of taxonomy, this work shows this possibility by developing a solution to this problem. In conclusion, this work proposes a solution to classify scientific content based on the title, abstract, and keywords through Natural Language Processing techniques and Machine Learning algorithms to organize scientific content in scientific domains.