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Title:Weakly-supervised text classification
Author(s):Meng, Yu
Advisor(s):Han, Jiawei
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Text Classification
Weakly-supervised Learning
Neural Classification Model
Hierarchical Classification
Abstract: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 semi-supervised and weakly-supervised text classification models exist, they cannot be easily applied to deep neural models and meanwhile support limited supervision types. In this work, we propose a weakly-supervised framework that addresses the lack of training data in neural text classification. Our framework 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 framework has the flexibility to handle different types of weak supervision and can be easily integrated into existing deep neural models for text classification. Based on this framework, we propose two methods, WeSTClass and WeSHClass, for flat text classification and hierarchical text classification, respectively. We have performed extensive experiments on real-world datasets from different domains. The results demonstrate that our proposed framework achieves inspiring performance without requiring excessive training data and outperforms baselines significantly.
Issue Date:2019-04-22
Rights Information:Copyright 2019 Yu Meng
Date Available in IDEALS:2019-08-23
Date Deposited:2019-05

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