… than 150 deep learning–based models for text classification developed in … for text classification.
Finally, we provide a quantitative analysis of the performance of different deep learning …

… newer methods of machine learning for document classification taken from deep learning. …
of deep learning approaches to create a hierarchical document classification approach. These …

… to text classification, including traditional and deep learning … text classification datasets in
Section 4. Finally, we summarize the main challenges for deep learning text classification in …

… e great successes of deep learning in multi-class classi cation and other related … use deep
learning to advance the state of the art in XMTC? Speci cally, how can we make deep learning …

… Traditional text classification methods based on machine learning have many … Based on
deep learning text classification, this paper presents an extensive study on the text classification …

… to text classification, including traditional and deep learning … text classification datasets in
Section 4. Finally, we summarize the main challenges for deep learning text classification in …

… Although the work is offered to provide some evidence-based guidance for practitioners
selecting word embeddings for text classification in deep models, there is still quite a list of work …

… (NLP), including words semantic classification, sentiment analysis, question answering, or
… architectures of deep learning models for the tasks of text classification: Deep Belief Neural (…

… Abstract—This work presents a method of classification of text documents using deep … We
evaluated feasibility of this approach for the task of subject classification of documents using a …

… underestimated fact that DNN-based text classification is surprisingly vulnerable to adversarial
… Black-box generation of adversarial text sequences to evade deep learning classifiers. …