Cluster-Gated Convolutional Neural Network for Short Text Classification | |
Zhang HD(张海东); Ni WC(倪晚成); Zhao MJ(赵美静); Lin ZQ(林子琦) | |
2019 | |
会议日期 | 2019-11-3 |
会议地点 | 香港 |
英文摘要 | 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. |
会议录 | Proceedings of the 23rd Conference on Computational Natural Language Learning |
会议录出版者 | Association for Computational Linguistics |
会议录出版地 | 香港 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/41467] |
专题 | 智能系统与工程 |
通讯作者 | Ni WC(倪晚成) |
作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhang HD,Ni WC,Zhao MJ,et al. Cluster-Gated Convolutional Neural Network for Short Text Classification[C]. 见:. 香港. 2019-11-3. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论