Text classification using embeddings: a survey

Text classification results can be hindered when just the bag-of-words model is used for representing features, because it ignores word order and senses, which can vary with the context. Embeddings have recently emerged as a means to circumvent these limitations, allowing considerable performance gains. However, determining the best combinations of classification techniques and embeddings for classifying particular corpora can be challenging. This survey provides a comprehensive review of text classification approaches that employ embeddings. First, it analyzes past and recent advancements in feature representation for text classification. Then, it identifies the combinations of embedding-based feature representations and classification techniques that have provided the best performances for classifying text from distinct corpora, also providing links to the original articles, source code (when available) and data sets used in the performance evaluation. Finally, it discusses current challenges and promising directions for text classification research, such as cost-effectiveness, multi-label classification, and the potential of knowledge graphs and knowledge embeddings to enhance text classification.