Zero-shot text classification using hierarchical novelty detection.

Abstract

Zero-shot hierarchical text classification, which classifies a text into a class in a hierarchy consisting of both seen and unseen classes, is important in wide applications such as news recommendation and product categorization. Two existing approaches, (1) matching approach and (2) hierarchical classification-based approach, have different performance characteristics on seen and unseen classes: matching approach performs well on unseen classes but worse on seen classes and vice versa in hierarchical classification-based approach. In this paper, we propose a zero-shot hierarchical text classification method that combines and generalizes two approaches to improve the performance on both seen and unseen classes. Experiments results on real-world datasets demonstrate the superiority of our proposed method over the baselines.