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        <title>Cluster-Gated Convolutional Neural Network for Short Text Classification</title>
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        <namePart type="given">Haidong</namePart>
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            <namePart type="given">Mohit</namePart>
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    <abstract>Text classification plays a crucial role for understanding natural language in a wide range of applications. Most existing approaches mainly focus on long text classification (e.g., blogs, documents, paragraphs). However, they cannot easily be applied to short text because of its sparsity and lack of context. In this paper, we propose a new model called cluster-gated convolutional neural network (CGCNN), which jointly explores word-level clustering and text classification in an end-to-end manner. Specifically, the proposed model firstly uses a bi-directional long short-term memory to learn word representations. Then, it leverages a soft clustering method to explore their semantic relation with the cluster centers, and takes linear transformation on text representations. It develops a cluster-dependent gated convolutional layer to further control the cluster-dependent feature flows. Experimental results on five commonly used datasets show that our model outperforms state-of-the-art models.</abstract>
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%0 Conference Proceedings
%T Cluster-Gated Convolutional Neural Network for Short Text Classification
%A Zhang, Haidong
%A Ni, Wancheng
%A Zhao, Meijing
%A Lin, Ziqi
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F zhang-etal-2019-cluster
%X Text classification plays a crucial role for understanding natural language in a wide range of applications. Most existing approaches mainly focus on long text classification (e.g., blogs, documents, paragraphs). However, they cannot easily be applied to short text because of its sparsity and lack of context. In this paper, we propose a new model called cluster-gated convolutional neural network (CGCNN), which jointly explores word-level clustering and text classification in an end-to-end manner. Specifically, the proposed model firstly uses a bi-directional long short-term memory to learn word representations. Then, it leverages a soft clustering method to explore their semantic relation with the cluster centers, and takes linear transformation on text representations. It develops a cluster-dependent gated convolutional layer to further control the cluster-dependent feature flows. Experimental results on five commonly used datasets show that our model outperforms state-of-the-art models.
%R 10.18653/v1/K19-1094
%U https://aclanthology.org/K19-1094
%U https://doi.org/10.18653/v1/K19-1094
%P 1002-1011

Markdown (Informal)

[Cluster-Gated Convolutional Neural Network for Short Text Classification](https://aclanthology.org/K19-1094) (Zhang et al., CoNLL 2019)

• Cluster-Gated Convolutional Neural Network for Short Text Classification (Zhang et al., CoNLL 2019)

ACL

• Haidong Zhang, Wancheng Ni, Meijing Zhao, and Ziqi Lin. 2019. Cluster-Gated Convolutional Neural Network for Short Text Classification. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 1002–1011, Hong Kong, China. Association for Computational Linguistics.