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Research on News Text Classification Based on Hybrid Model

Published: 14 March 2022 Publication History

Abstract

With the rapid development and wide application of information technology and Internet, the amount of various information data is growing explosively. News text information is a more extensive form of text information that people have access to. Using computer to screen and classify these news information effectively can quickly and efficiently obtain valuable information content. With the development of machine learning, text classification has been gradually transferred from manual operation to machine automation. It is a basic step of machine learning to use the classified text information to learn the category features and then classify the unclassified text. Text automatic classification method are many, for the improvement of text automatic classification algorithm, its purpose lies in how to closer to the human way of thinking on the text information classification, the classification results of this can better meet the needs of people for text classification, also convenient and rapid access to valuable information. In this paper, a hybrid model is used to achieve better classification results.

References

[1]
Vaswani A, Shazeer N, Parmar N,et al. Attention is all you need. Proceedings of the 31st Conference on Neural Information Processing Systems. Long Beach,CA,USA.2017.5998-6008
[2]
Rao A, Spasojevic N. Actionable and political text classification using word embeddings and LSTM.arXiv:1607.02501, 2016.
[3]
Peters M E, Neumann M, Iyyer M, Deep Contextualized Word Representations. arXiv: Computation and Language, 2018.
[4]
Kim Y. Convolutional Neural Networks for Sentence Classification. Empirical Methods in Natural Language Processing, 2014:1746-1751.
[5]
Che Z, Purushotham S, Cho K, Recurrent Neural Networks for Multivariate Time Series with Missing Values. Scientific Reports, 2017, 8(1):6085-6085.
[6]
Pan J, Yin Y, Xiong J, Deep Learning-Based Unmanned Surveillance Systems for Observing Water Levels. IEEE Access, 2018:73561-73571.
[7]
Huang W, Qiao Y, Tang X, Robust Scene Text Detection with Convolution Neural Network Induced MSER Trees. European Conference on Computer Vision, 2014:497-511.
[8]
Lecun Y, Bengio Y, Hinton G E, Deep learning. Nature, 2015, 521(7553):436-444.
[9]
Collobert R, Weston J. A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning. International Conference on Machine Learning, 2008:160-167.
[10]
Bengio Y, Ducharme R, Vincent P, A Neural Probabilistic Language Model. Journal of Machine Learning Research, 2003, 3(6):1137-1155.

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  • (2024)A deep learning approach for robust traffic accident information extraction from online chinese newsIET Intelligent Transport Systems10.1049/itr2.1249318:10(1847-1862)Online publication date: 22-Feb-2024
  1. Research on News Text Classification Based on Hybrid Model

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    cover image ACM Other conferences
    AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture
    October 2021
    3136 pages
    ISBN:9781450385046
    DOI:10.1145/3495018
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    Published: 14 March 2022

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    • (2024)A deep learning approach for robust traffic accident information extraction from online chinese newsIET Intelligent Transport Systems10.1049/itr2.1249318:10(1847-1862)Online publication date: 22-Feb-2024

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