Weakly-Supervised Hierarchical Text Classification

By Yu Meng, Jiaming Shen, Chao Zhang, Jiawei Han. 2018

141 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jiawei Han
2. Yu Meng
3. Chao Zhang
4. Jiaxin Huang
5. Yu Zhang
6. Jieyu Zhang
7. Yue Yu
8. Tarek F. Abdelzaher

In Natural Language Processing

1. Yu Meng
2. Jiawei Han
3. Jiaxin Huang
4. Yu Zhang
5. Alexander J. Ratner
6. Ranjay Krishna
7. Chao Zhang
8. Yue Yu

Citations in the corpus

• Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias (2023, background): “Thus, we consider the following datasets from various domains with the number of topics ranging from 23 to 50 4 : • NYT [31]: The NYT dataset comprises news articles that were authored and published by The New York Times .”
• Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning (2022, background): “More specifically related to the idea of generating training data with language models, early meth-ods in text classification use bag-of-words or LSTM-based language models (Meng et al., 2018; 2019) to generate class-conditioned texts as training data.”
• Retrieval And Structuring Augmented Generation with Large Language Models (2025, unknown): “WeSHClass [166] extends WeSTClass [165] by modeling label hierarchies as mixtures of distributions and using self-training.”