Effective Seed-Guided Topic Labeling for Dataless Hierarchical Short Text Classification

Abstract

Hierarchical text classification has a wide application prospect on the Internet, which aims to classify texts into a given hierarchy. Supervised methods require a large amount of labeled data and are thus costly. For this purpose, the task of dataless hierarchical text classification has attracted more and more attention of researchers in recent years, which only requires a few relevant seed words for given categories. However, existing approaches mainly focus on long texts without considering the characteristics of short texts, so are not suitable in many scenarios. In this paper, we tackle dataless hierarchical short text classification for the first time, and propose an innovative model named Hierarchical Seeded Biterm Topic Model (HierSeedBTM), which effectively leverages seed words in Biterm Topic Model (BTM) to guide the hierarchical topic labeling. Specifically, our model introduces iterative distribution propagation mechanism among topic models in different levels to incorporate the hierarchical structure information. Experiments on two public datasets show that the proposed model is more effective than the state-of-the-art methods of dataless hierarchical text classification designed for long texts.