Learning Refined Features for Open-World Text Classification

Open-world classification requires a classifier not only to classify samples of the observed classes but also to detect samples which are not suitable to be classified as the known classes. State-of-the-art methods train a network to extract features for separating known classes firstly. Then some strategies, such as outlier detector, are used to reject samples from unknown classes based on the feature space. However, this network as a feature extractor cannot model comprehensive features of known classes in an open world scenario due to limited training data. This causes a problem that the strategies are unable to separate unknown classes from known classes accurately in this feature space. Motivated by the theory of psychology and cognitive science, we utilize class descriptions summarized by human to refine discriminant features and propose a regularization with class descriptions. The regularization is incorporated into DOC (one of state-of-the-art models) to improve the performance of open-world classification. The experiments on two text classification datasets demonstrate the effectiveness of the proposed method.