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Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding
lib:440dabaafc9ca5cd (v1.0.0)
Authors: Rie Johnson,Tong Zhang
Where published: NeurIPS 2015 12
ArXiv: 1504.01255
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1504.01255v3
This paper presents a new semi-supervised framework with convolutional neural networks (CNNs) for text categorization. Unlike the previous approaches that rely on word embeddings, our method learns embeddings of small text regions from unlabeled data for integration into a supervised CNN. The proposed scheme for embedding learning is based on the idea of two-view semi-supervised learning, which is intended to be useful for the task of interest even though the training is done on unlabeled data. Our models achieve better results than previous approaches on sentiment classification and topic classification tasks.
Where published: NeurIPS 2015 12
ArXiv: 1504.01255
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1504.01255v3
This paper presents a new semi-supervised framework with convolutional neural networks (CNNs) for text categorization. Unlike the previous approaches that rely on word embeddings, our method learns embeddings of small text regions from unlabeled data for integration into a supervised CNN. The proposed scheme for embedding learning is based on the idea of two-view semi-supervised learning, which is intended to be useful for the task of interest even though the training is done on unlabeled data. Our models achieve better results than previous approaches on sentiment classification and topic classification tasks.
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