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. Bagofword 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 stateoftheart. And, we propose an original comparison between Incremental learning and Deep learning, by comparing the incremental system with a CCNRNNbased approach. It demonstrates that both approaches are competitive in similar contest.

A Robust Hybrid Approach for Textual Document Classification Text document classification is an important task for diverse natural language processing based applications. Traditional machine learning approaches mainly focused on reducing dimensionality of textual data to perform classification. This although improved the overall classification accuracy, the classifiers still faced sparsity problem due to lack of better data representation techniques. Deep learning based text document classification, on the other hand, benefitted greatly from the invention of word embeddings that have solved the sparsity problem and researchers focus mainly remained on the development of deep architectures. Deeper architectures, however, learn some redundant features that limit the performance of deep learning based solutions. In this paper, we propose a two stage text document classification methodology which combines traditional feature engineering with automatic feature engineering (using deep learning). The proposed methodology comprises a filter based feature selection (FSE) algorithm followed by a deep convolutional neural network. This methodology is evaluated on the two most commonly used public datasets, i.e., 20 Newsgroups data and BBC news data. Evaluation results reveal that the proposed methodology outperforms the stateoftheart of both the (traditional) machine learning and deep learning based text document classification methodologies with a significant margin of 7.7% on 20 Newsgroups and 6.6% on BBC news datasets.