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SF-CNN: Deep Text Classification and Retrieval for Text Documents

R. Sarasu1,*, K. K. Thyagharajan2, N. R. Shanker3

1 Computer Science and Engineering, Dhanalaksmi College of Engineering, Anna University, Chennai, India
2 R. M. D Engineering College, Anna University, Chennai, India
3 Computer Science and Engineering, Aalim Muhammed Salegh College of Engineering, Anna University, Chennai, India

* Corresponding Author: R. Sarasu. Email: email

Intelligent Automation & Soft Computing 2023, 35(2), 1799-1813.


Researchers and scientists need rapid access to text documents such as research papers, source code and dissertations. Many research documents are available on the Internet and need more time to retrieve exact documents based on keywords. An efficient classification algorithm for retrieving documents based on keyword words is required. The traditional algorithm performs less because it never considers words’ polysemy and the relationship between bag-of-words in keywords. To solve the above problem, Semantic Featured Convolution Neural Networks (SF-CNN) is proposed to obtain the key relationships among the searching keywords and build a structure for matching the words for retrieving correct text documents. The proposed SF-CNN is based on deep semantic-based bag-of-word representation for document retrieval. Traditional deep learning methods such as Convolutional Neural Network and Recurrent Neural Network never use semantic representation for bag-of-words. The experiment is performed with different document datasets for evaluating the performance of the proposed SF-CNN method. SF-CNN classifies the documents with an accuracy of 94% than the traditional algorithms.


Cite This Article

R. Sarasu, K. K. Thyagharajan and N. R. Shanker, "Sf-cnn: deep text classification and retrieval for text documents," Intelligent Automation & Soft Computing, vol. 35, no.2, pp. 1799–1813, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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