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arXiv · 1809.03664

Topic Memory Networks for Short Text Classification

Jichuan Zeng·Jing Li·Yan Song·Cuiyun Gao·Michael R. Lyu·Irwin King
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Abstract

Many classification models work poorly on short texts due to data sparsity. To address this issue, we propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels. Different from most prior work that focuses on extending features with external knowledge or pre-trained topics, our model jointly explores topic inference and text classification with memory networks in an end-to-end manner. Experimental results on four benchmark datasets show that our model outperforms state-of-the-art models on short text classification, meanwhile generates coherent topics.

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Jichuan Zeng, Jing Li, Yan Song, Cuiyun Gao, Michael R. Lyu, Irwin King. 2018-09-11. Topic Memory Networks for Short Text Classification. https://arxiv.org/abs/1809.03664

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