Research Article
Document Classification by Using Hybrid Deep Learning Approach
386 downloads
@INPROCEEDINGS{10.1007/978-3-030-34365-1_13, author={Bui Hung}, title={Document Classification by Using Hybrid Deep Learning Approach}, proceedings={Context-Aware Systems and Applications, and Nature of Computation and Communication. 8th EAI International Conference, ICCASA 2019, and 5th EAI International Conference, ICTCC 2019, My Tho City, Vietnam, November 28-29, 2019, Proceedings}, proceedings_a={ICCASA \& ICTCC}, year={2019}, month={12}, keywords={Document classification CNN LSTM Hybrid deep learning}, doi={10.1007/978-3-030-34365-1_13} }- Bui Hung
Year: 2019
Document Classification by Using Hybrid Deep Learning Approach
ICCASA & ICTCC
Springer
DOI: 10.1007/978-3-030-34365-1_13
Abstract
Text classification is an essential component in a variety of applications of natural language processing. While the deep learning-based approach is becoming more popular, using vectors of word as an input for the models has proved to be a good way for the machine to learn the relation between words in a document. This paper proposes a solution for the text classification using hybrid deep learning approaches. Every existing deep learning approach has its own advantages and the hybrid deep learning model we are introducing is the combination of the superior features of CNN and LSTM models. The proposed models CNN-LSTM, LSTM-CNN show enhanced accuracy over another approach.
Copyright © 2019–2024 ICST
