{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-attention-gated-convolutional-neural","title":"An Attention-Gated Convolutional Neural Network for Sentence Classification","arxiv_id":"1808.07325","date":"2018-08-22","proceeding":null,"authors":["Yang Liu","Lixin Ji","Ruiyang Huang","Tuosiyu Ming","Chao GAO","Jianpeng Zhang"],"abstract":"The classification of sentences is very challenging, since sentences contain\nthe limited contextual information. In this paper, we proposed an\nAttention-Gated Convolutional Neural Network (AGCNN) for sentence\nclassification, which generates attention weights from the feature's context\nwindows of different sizes by using specialized convolution encoders. It makes\nfull use of limited contextual information to extract and enhance the influence\nof important features in predicting the sentence's category. Experimental\nresults demonstrated that our model can achieve up to 3.1% higher accuracy than\nstandard CNN models, and gain competitive results over the baselines on four\nout of the six tasks. Besides, we designed an activation function, namely,\nNatural Logarithm rescaled Rectified Linear Unit (NLReLU). Experiments showed\nthat NLReLU can outperform ReLU and is comparable to other well-known\nactivation functions on AGCNN.","url_abs":"http://arxiv.org/abs/1808.07325v3","url_pdf":"http://arxiv.org/pdf/1808.07325v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"an-attention-gated-convolutional-neural","repo_url":"https://github.com/fabyangliu/AGCNN-Sentence-Classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"an-attention-gated-convolutional-neural","repo_url":"https://github.com/fabyangliu/AGCNN_sentence_classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-classification","task_name":"Sentence Classification"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"relu","method_name":"ReLU"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}