Classic and recent (neural) approaches to automatic text classification

Abstract

Currently, millions of data are generated daily and its exploitation and interpretation has become essential at every scope. However, most of this information is in textual format, lacking the structure and organisation of traditional databases, which represents an enormous challenge to overcome. Over the course of time, different approaches have been proposed for text representation attempting to better capture the semantic of documents. They included classic information retrieval approaches (like Bag of Words) to new approaches based on neural networks such as basic word embeddings, deep learning architectures (LSTMs and CNNs), and contextualized embeddings based on attention mechanisms (Transformers). Unfortunately, most of the available resources supporting those technologies are English-centered. In this work, using an e-mail-based study case, we measure the performance of the three most important machine learning approaches applied to the text classification, in order to verify if new arrivals enhance the results from the Spanish language classification models.