Abstract
This letter discusses the limitations of the use of filters to enhance the accuracy of the extraction of parenthetic abbreviations from scholarly publications and proposes the usage of the parentheses level count algorithm to efficiently extract entities between parentheses from raw texts as well as of machine learning-based supervised classification techniques for the identification of biomedical abbreviations to significantly reduce the removal of acronyms including disallowed punctuations.
Mohamed Ali Hadj Taieb
Assistant professor
My research interests include semantic similarity, semantic relatedness, knowledge representation, Big Data, social media, data management systems and graph embedding.
Mohamed Ben Aouicha
Professor
My research interests concern information retrieval, semantic technologies, social media analytics, knowledge representation, Big Data and graph embedding.