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Nagarajan, R.
- Improved Feature Set Extraction From Documents Using Modified Bag Of Words
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Authors
Affiliations
1 Department of Computer and Information Science, Annamalai University, IN
1 Department of Computer and Information Science, Annamalai University, IN
Source
ICTACT Journal on Soft Computing, Vol 11, No 1 (2020), Pagination: 2213-2217Abstract
In conventional literatures, there are several different methods of collection and extraction and are also used to minimize dimensionality. Traditional methods are intuitively designed to delete redundant and outdated information to help define new test cases more effectively. But the number of specific words in the Bag of Words (BoW) model must be manually calculated, requiring time and work and portability of deficiencies. In addition, the number of codebook vectors in BoW rises as cancer types grow and the efficiency and accuracy of detection are reduced. The BoW model is therefore not ideal for multi-operative failure diagnosis. Therefore, we propose an improved BoW in this paper which selects the number of special terms required to collect cancer diagnostic functions from different documents. The overall recognition and accuracy rates are higher than other existing extraction models. The improved BoW method has been verified to be highly effective in operating conditions that meet the requirements in real time.Keywords
Bag of Words, Cancer Document Retrieval, Codebook, Dimensionality Reduction.
- Improved Feature Extraction on Text Documents using Neural Network Model
Abstract Views :190 |
PDF Views:0
Authors
V. Kumaresan
1,
R. Nagarajan
1
Affiliations
1 Department of Computer and Information Science, Annamalai University, IN
1 Department of Computer and Information Science, Annamalai University, IN
Source
ICTACT Journal on Soft Computing, Vol 11, No 2 (2021), Pagination: 2279-2282Abstract
In natural language processing, the text clustering plays a major role on reducing the text dimensionality. However, the lack of data models has made the clustering algorithm to face sparsity problems. The integration with deep learning has resolved the problem of scarce knowledge on text documents. However, deeper architectures learn such redundant features, which limit the efficiency of solutions. In this paper, a complete extraction of features from text document using neural network model. The neural network model utilizes feed forward mechanism and a type of unsupervised learning that denoises the corrupted input features. The reconstructed feature is used for initialing the feed forward network. This method reduces the manual labelling in the process of screening. For evaluation, series of experiments are conducted to investigate the performance of the method over the text datasets with various conventional algorithms.Keywords
Text Document, Feature Extraction, Neural Network, Denoising.References
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