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Multi-level Teaching Text Classification based on the Fusion of Deep Learning Models

Published: 30 May 2024 Publication History

Abstract

Deep learning is widely used in text classification, which can be help the collection of teaching text samples from multiple datasets. On the other side, the variety of texts causes the difficulty of the classification. To solve this problem, we design a multi-level text classification system that fuses multiple models to increase the accuracy of the classification. In more details, we train the deep learning models on some public datasets. Then, we organize these to construct multi-level sets of models. On a text sample, we firstly detect if the dataset classification is needed. Then, we send this sample to the proper level of model set. Finally, we continuously optimize the system based on the collected samples. As the experimental results show, our method can achieve higher accuracy than the existing ones.

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    ICIEAI '23: Proceedings of the 2023 International Conference on Information Education and Artificial Intelligence
    December 2023
    1132 pages
    ISBN:9798400716157
    DOI:10.1145/3660043
    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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    Published: 30 May 2024

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