摘要
Text classification is a foundational task in natural language processing (NLP). Traditional methods rely heavily on human-designed features, while deep learning models based on neural networks can automatically capture contextual information. We explore and introduce various neural network architectures to extract information and key components in texts. An extensive set of experiments and comparisons on accuracy, speed, memory-consumption are conducted. Methods based on the proposed models won the first place in the Zhihu Machine Learning Challenge 2017. The code has been made publicly available.
源语言 | 英语 |
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主期刊名 | Proceedings of 2018 6th IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2018 |
出版商 | Institute of Electrical and Electronics Engineers Inc. |
页 | 409-413 |
页数 | 5 |
ISBN(电子版) | 9781538660669 |
DOI | |
出版状态 | 已出版 - 6 11月 2018 |
活动 | 6th IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2018 - Guiyang, 中国 期限: 22 8月 2018 → 24 8月 2018 |
出版系列
姓名 | Proceedings of 2018 6th IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2018 |
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会议
会议 | 6th IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2018 |
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国家/地区 | 中国 |
市 | Guiyang |
时期 | 22/08/18 → 24/08/18 |
指纹
探究 'Multi-label Text Classification with Deep Neural Networks' 的科研主题。它们共同构成独一无二的指纹。引用此
Chen, Y., Xiao, B., Lin, Z., Dai, C., Li, Z., & Yan, L. (2018). Multi-label Text Classification with Deep Neural Networks. 在 Proceedings of 2018 6th IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2018 (页码 409-413). 文章 8525817 (Proceedings of 2018 6th IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICNIDC.2018.8525817