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Multi-Level Text Importance Classification Architecture Based on Deep Learning

Published: 07 November 2023 Publication History

Abstract

In the era of information explosion, the Internet is full of spam and false information, making it more difficult for people to obtain effective information. Since text data is the main carrier for disseminating information and knowledge, we propose a multi-level text importance classification architecture based on deep learning to enable Internet users to quickly and accurately access text content of interest. Experiments demonstrate that the proposed architecture can achieve a good performance.

References

[1]
Kim Y . Convolutional Neural Networks for Sentence Classification[J]. Eprint Arxiv, 2014.
[2]
Vaswani A, Shazeer N, Parmar N, Attention Is All You Need[J]. arXiv, 2017.
[3]
Joulin A, Grave E, Bojanowski P, Bag of Tricks for Efficient Text Classification[J]. 2017.
[4]
Graves A, Jürgen Schmidhuber. Framewise phoneme classification with bidirectional LSTM and other neural network architectures[J]. Neural Networks, 2005, 18( 5–6):602-610.

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  1. Multi-Level Text Importance Classification Architecture Based on Deep Learning

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    APNet '22: Proceedings of the 6th Asia-Pacific Workshop on Networking
    July 2022
    110 pages
    ISBN:9781450397483
    DOI:10.1145/3542637
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 07 November 2023

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