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Reseach Article

Text Document Classification by using WordNet Ontology and Neural Network

by Manisha Gawade, Tejashree Mane, Dhanashree Ghone, Prasad Khade, Nihar Ranjan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 33
Year of Publication: 2018
Authors: Manisha Gawade, Tejashree Mane, Dhanashree Ghone, Prasad Khade, Nihar Ranjan
10.5120/ijca2018918229

Manisha Gawade, Tejashree Mane, Dhanashree Ghone, Prasad Khade, Nihar Ranjan . Text Document Classification by using WordNet Ontology and Neural Network. International Journal of Computer Applications. 182, 33 ( Dec 2018), 33-36. DOI=10.5120/ijca2018918229

@article{ 10.5120/ijca2018918229,
author = { Manisha Gawade, Tejashree Mane, Dhanashree Ghone, Prasad Khade, Nihar Ranjan },
title = { Text Document Classification by using WordNet Ontology and Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2018 },
volume = { 182 },
number = { 33 },
month = { Dec },
year = { 2018 },
issn = { 0975-8887 },
pages = { 33-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number33/30246-2018918229/ },
doi = { 10.5120/ijca2018918229 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:13:13.080196+05:30
%A Manisha Gawade
%A Tejashree Mane
%A Dhanashree Ghone
%A Prasad Khade
%A Nihar Ranjan
%T Text Document Classification by using WordNet Ontology and Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 33
%P 33-36
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Every day the mass of information available, merely finding the relevant information is not the only task of automatic text classification systems. The main problem is to classify which documents are relevant and which are irrelevant. The Automated text classification consists of automatically organizing clustered data. We propose a method of automatic text classification using Convolutional Neural Network based on the disambiguation of the meaning of the word we use the WordNet ontology and word embedding algorithm to eliminate the ambiguity of words so that each word is replaced by its meaning in suitable context. The closest ancestors of the senses of all the words in a given document are selected as folders for the specified document.

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Index Terms

Computer Science
Information Sciences

Keywords

neural network classification wordsense feature selection model selection WordNet.