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.
Weakly-Supervised Neural Text Classification
Deep neural networks are gaining increasing popularity for the
classic text classification task, due to their strong expressive power
and less requirement for feature engineering. Despite such attractiveness,
neural text classification models suffer from the lack of
training data in many real-world applications. Although many semisupervised
and weakly-supervised text classification models exist,
they cannot be easily applied to deep neural models and meanwhile
support limited supervision types. In this paper, we propose a
weakly-supervised method that addresses the lack of training data
in neural text classification. Our method consists of two modules:
(1) a pseudo-document generator that leverages seed information
to generate pseudo-labeled documents for model pre-training, and
(2) a self-training module that bootstraps on real unlabeled data for
model refinement. Our method has the flexibility to handle different
types of weak supervision and can be easily integrated into existing
deep neural models for text classification. We have performed extensive
experiments on three real-world datasets from different domains.
The results demonstrate that our proposed method achieves
inspiring performance without requiring excessive training data
and outperforms baseline methods significantly.
- Publication Date:
- NSF-PAR ID:
- 10079169
- Journal Name:
- Proceedings of the 27th {ACM} International Conference on Information and Knowledge Management, {CIKM} 2018
- Volume:
- 2018
- Issue:
- 1
- Page Range or eLocation-ID:
- 983 to 992
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1145/3269206.3271737
