Open Access
Issue |
ITM Web Conf.
Volume 47, 2022
2022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
|
|
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Article Number | 02040 | |
Number of page(s) | 9 | |
Section | Algorithm Optimization and Application | |
DOI | https://doi.org/10.1051/itmconf/20224702040 | |
Published online | 23 June 2022 |
- KIM Y. Convolutional neural networks for sentence classification [J] arXiv preprint ar Xiv: 1408. 5882,2014. [Google Scholar]
- JORDAN M.I.A parallel distributed processing approach [J], Advances in Psychology, 1997, 121: 471–495. [CrossRef] [Google Scholar]
- SCHMIDHUBE R J. Deep learning in neural networks: an verview [J], Neural Networks, 2015, 61: 85–117. [CrossRef] [Google Scholar]
- GRAVES a Hmidhuber J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures [J].Neural Networks, 2005, 18 (5–6): 602–610. [CrossRef] [Google Scholar]
- CHO K, MERRIENBOER BV, BAHDANAU D, et.al. On the properties of neural machine translation: Encoder-decoder approaches[j].arXiv preprint arXiv: 1409,1259,2014. [Google Scholar]
- Wu Xiaohua, Chen Li, Wei Tiantian, et al. Emotion analysis of Chinese short text based on self attention and Bi LSTM [J]. Chinese Journal of information technology, 2019, 33 (6): 100–107. [Google Scholar]
- Li Lihua, Hu Xiaolong Text emotion analysis based on deep learning [J] Journal of Hubei University: Natural Science Edition, 2020, 42 (2): 142–149. [Google Scholar]
- Cai J, Li J, Li W, et al. Deeplearning model used in text classification[C]//2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2018: 123–126. [Google Scholar]
- LAI SW,XUL H,LIU K,et al. Recurrent convolutional neural networks for text classification[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015:2267–2273. [Google Scholar]
- ZHANG J R,LI Y X,TIAN J,et al. LSTM-CNN hybrid model for text classification[C]// Proceedings of the 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference. 2018: 1675–1680. [Google Scholar]
- SHE X Y, ZHANG D. Text classification based on hybrid CNN-LSTM hybrid model[C]//Proceedings of the 2018 11th International Symposium on Computational Intelligence and Design.2018.DOI: 10.1109/ISCID.2018.10144. [Google Scholar]
- LI C B,ZHAN G H,LI Z H. News text classification based on improved Bi-LSTM-CNN[C]//Proceedings of the 2018 9th International Conference on Information Technology in Medicine and Education. 2018: 890–893. [Google Scholar]
- WANG G Y, LI C Y, WANG W L,et al. Joint embedding of words and labels for text classification[J].arXiv preprint ar Xiv: 1805.04174,2018. [Google Scholar]
- LUO L X Network text sentiment analysis method combining LDA text representation and GRU-CNN [J].Personal and Ubquitous Computing, 2019, 23 (3–4): 405–412. [CrossRef] [Google Scholar]
- ZHANG Y S, ZHENG J, JIANG Y, et al. A text sentiment classification modeling method based on coordinated CNN-LSTM-Attention model [J]. Chinese Journal of Electronics, 2019, 28(01): 120–126. [CrossRef] [Google Scholar]
- Jiang M, Zhang W, Zhang M, et al. An LSTM-CNN attention approach for aspect-level sentiment classification [J]. Journal of Computational Methods in Sciences and Engineering, 2019, 19(4): 859–868. [CrossRef] [Google Scholar]
- LI C H, ZHANG C Y, FU Q. Research on CNN+LSTM user intention classification based on multi-granularity features of texts [J]. The Journal of Engineering, 2020, 2020(13). [Google Scholar]
- Li X, Cui M, Li J, et al. A hybrid medical text classification framework: Integrating attentive rule construction and neural network [J]. Neurocomputing, 2021, 443: 345–355. [CrossRef] [Google Scholar]
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