Papers › Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding

Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding

6 Apr 2015NeurIPS 2015 12arXiv:1504.01255archive 2025-07-28

Rie Johnson, Tong Zhang

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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Tasks

Classification General Classification Sentiment Analysis Sentiment Classification Text Categorization Text Classification Topic Classification Word Embeddings

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