Title: Closed Boundary Learning for NLP Classification Tasks with the Universum Class. Abstract: The Universum class, often known as the other class or the miscellaneous class, is defined as a collection of samples that do not belong to any class of interest. It is a typical class that exists in many classification-based tasks in natural language processing (NLP), such as relation extraction, named entity recognition, sentiment analysis, etc. During data labeling, a significant number of samples are annotated as Universum because there are always some samples that exist in the dataset but do not belong to preset target classes and are not of interest in the task. The Universum class exhibits very different properties, namely heterogeneity and lack of representativeness in training data; however, existing methods often treat the Universum class equally with the classes of interest. Although the Universum class only contains uninterested samples, improper treatment will result in the misclassification of samples of interest. In this work, we propose a closed boundary learning method that treats the Universum class and classes of interest differently. We apply closed decision boundaries to classes of interest and designate the area outside all closed boundaries in the feature space as the space of the Universum class. Specifically, we formulate the closed boundaries as arbitrary shapes, propose a strategy to estimate the probability of the Universum class according to its unique property rather than the within-class sample distribution, and propose a boundary learning loss to learn decision boundaries based on the balance of misclassified samples inside and outside the boundary. We evaluate our method on 6 state-of-the-art works in 3 different tasks, and the performance of all 6 works is improved. Our code will be released on GitHub.