Comparing effectiveness of regularization methods on text classification: Simple and complex model in data shortage situation

arXiv:2403.00825v1 Announce Type: new
Abstract: Text classification is the task of assigning a document to a predefined class. However, it is expensive to acquire enough labeled documents or to label them. In this paper, we study the regularization methods' effects on various classification models when only a few labeled data are available. We compare a simple word embedding-based model, which is simple but effective, with complex models (CNN and BiLSTM). In supervised learning, adversarial training can further regularize the model. …

abstract arxiv class classification cs.cl data document documents effects paper regularization shortage simple study text text classification them type