Weakly-Supervised Hierarchical Text Classification
This addresses the problem of reducing data requirements for hierarchical text classification in real-world applications, though it is incremental as it builds on existing neural methods.
The paper tackles the challenge of hierarchical text classification with deep neural networks by proposing a weakly-supervised method that uses minimal weak supervision signals, such as a few documents or keywords, to generate pseudo documents and iteratively refine the model through self-training, achieving efficacy demonstrated on three datasets.
Hierarchical text classification, which aims to classify text documents into a given hierarchy, is an important task in many real-world applications. Recently, deep neural models are gaining increasing popularity for text classification due to their expressive power and minimum requirement for feature engineering. However, applying deep neural networks for hierarchical text classification remains challenging, because they heavily rely on a large amount of training data and meanwhile cannot easily determine appropriate levels of documents in the hierarchical setting. In this paper, we propose a weakly-supervised neural method for hierarchical text classification. Our method does not require a large amount of training data but requires only easy-to-provide weak supervision signals such as a few class-related documents or keywords. Our method effectively leverages such weak supervision signals to generate pseudo documents for model pre-training, and then performs self-training on real unlabeled data to iteratively refine the model. During the training process, our model features a hierarchical neural structure, which mimics the given hierarchy and is capable of determining the proper levels for documents with a blocking mechanism. Experiments on three datasets from different domains demonstrate the efficacy of our method compared with a comprehensive set of baselines.