The week's pick
Reseach Article
Text Document Classification by using WordNet Ontology and Neural Network
| 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
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.
References
- WangP,XuB,XuJ,etal. Semantic expansionusingword embedding clustering and convolutional neural network for improving short text classification. Neurocomputing. 2016;174:806–814.
- J.-T. Chien, “Hierarchical theme and topic modeling,” IEEE Trans. Neural Netw. Learn. Syst., vol. 27, no. 3, pp. 565–578, 2016.
- Bernardini, C. Carpineto, and M. D’Amico, “Full-subtopic retrieval with keyphrase-based search results clustering,” in IEEE/WIC/ACM Int. Joint Conf. Web Intell. Intelligent Agent Technol., 2009, pp. 206–213
- Lu Pan, Haibo Tang and Lei Zhou, Liuyang Wang, Quanyin Zhu, “An Identification Method of News Scientific Intelligence Based on TF-IDF” 2015 IEEE DOI 10.1109/DCABES.2015.131
- Ning Li, Hui Zhang, Yong Chen, “Convolutional Neural Network with SDP-based Attention for Relation Classification” 2018 IEEE DOI 10.1109/BigComp.2018.00108
- S. Dumais, J. Platt, D. Heckerman, and M. Sahami, “Inductive learning algorithms and representations for text categorization,” in Proc. Int. Conf. Inform. Knowl. Manag., 1998, pp. 148–155
- Ying Liu1, Peter Scheuermann2, Xingsen Li1, and Xingquan Zhu1Using WordNet to Disambiguate Word Senses for TextClassification.
- T. Kohonen, S. Kaski, K. Lagus, J. Salojarvi, J. Honkela, V. Paatero,and A. Saarela, “Self-organization of a massive document collection,”IEEE Trans. Neural Netw., vol. 11, no. 3, pp. 574–585, 2000.
- Q. Mei, X. Shen, and C. Zhai, “Automatic labeling of multinomial topic models,” in Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2007, pp. 490–499
- K. Lagus and S. Kaski, “Keyword selection method for characterizing text document maps,” in Int. Conf. Artificial Neural Networks (ICANN), 1999, pp. 371–376
- Nihalr M. Ranjan a,b,∗ Rajesh S. Prasad b “LFNN: Lion fuzzy neural network-based evolutionary model for text classification using context and sense based features ”https://doi.org/10.1016/j.asoc.2018.07.016 1568-4946/©2018 Published by Elsevier B.V
- Nihar M. Ranjan a and Rajesh S. Prasad b “Automatic text classification using BPLion-neural network and semantic word processing” THE IMAGING SCIENCE JOURNAL, 2017https://doi.org/10.1080/13682199.2017.1376781
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