Knowledge Commons of Institute of Automation,CAS
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 | |
会议名称 | 23rd Conference on Computational Natural Language Learning(CoNLL) |
会议录名称 | Proceedings of the 23rd Conference on Computational Natural Language Learning |
会议日期 | 2019-11-3 |
会议地点 | 香港 |
出版地 | 香港 |
出版者 | Association for Computational Linguistics |
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. |
收录类别 | SCI |
七大方向——子方向分类 | 自然语言处理 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/41467 |
专题 | 复杂系统认知与决策实验室_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]. 香港:Association for Computational Linguistics,2019. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
2019-CoNLL.pdf(1310KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 |
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