Discourse Descriptor for Document Incremental Classification Comparison with Deep Learning

We propose both a new strategy to weight a text vector for document classification and a comparison of a deep learning approach versus an incremental classification approach, integrating our novel strategy. Bag-of-word vectors are classic approaches to describe a textual document in document classification objective. A weakness of the bag of words is to lose the organization of the discourse within the document. Inspired by some Deep Learning approaches and Natural Language Processing for text classification, we suggest a simple strategy, featuring the terms according to their relative positions within the discourse sequence. For experimentations, we apply this strategy to a recent document incremental classification approach from the state-of-the-art. And, we propose an original comparison between Incremental learning and Deep learning, by comparing the incremental system with a CCN-RNN-based approach. It demonstrates that both approaches are competitive in similar contest.