Learn: A Novel Incremental Learning Method for Text Classification Abstract   Deep learning is an effective method for extracting the underlying information in text. However, it performs better on closed datasets and is less effective in realworld scenarios for text classification. As the data is updated and the amount of data increases, the models need to be retrained, in what is often a long training process. Therefore, we propose a novel incremental learning strategy to solve these problems. Our method, called Learn, includes four components: a Student model, a reinforcement learning (RL) module, a Teacher model, and a discriminator model. The Student models first extract the features from the texts, then the RL module filters the results of multiple Student models. After that, the Teacher model reclassifies the filtered results to obtain the final texts category. To avoid increasing the Student models unlimitedly as the number of samples increases, the discriminator model is used to filter the Student models based on their similarity. The Learn method has the advantage of a shorter training time than the OneTime model, because it only needs to train a new Student model each time, without changing the existing Student models. Furthermore, it can also obtain feedback during application and tune the models parameters over time. Experiments on different datasets show that our method for text classification outperforms many traditional OneTime methods, reducing training time by nearly 80%.

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.