Semantic Based Features Selection and Weighting Method for Text Classification
Feature selection and weighting is of vital concern in text classification process which improves the efficiency and accuracy of text classifier. Vector Space Model is used to represent the documents using "Bag of Word" BOW model with term weighting phenomena. Documents representation...
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my.utp.eprints.64322017-01-19T08:24:35Z Semantic Based Features Selection and Weighting Method for Text Classification Aurangzeb , khan Baharum , Baharudin Khairullah , khan T Technology (General) Feature selection and weighting is of vital concern in text classification process which improves the efficiency and accuracy of text classifier. Vector Space Model is used to represent the documents using "Bag of Word" BOW model with term weighting phenomena. Documents representation through this model has some limitations that are, ignoring term dependencies, structure and ordering of the terms in documents. To overcome this problem, Semantics Base Feature Vector using Part of Speech (POS), is proposed, which is used to extract the concept of terms using WordNet, co-occurring and associated terms. The proposed method is applied on small documents dataset which shows that this method outperforms then term frequency/ inverse document frequency (TF-IDF) with BOW feature selection method for text classification. 2010 Conference or Workshop Item PeerReviewed application/pdf http://eprints.utp.edu.my/6432/1/Semantic_Based_Features_Selection_and_Weighting_method_for_text_classification.pdf application/pdf http://eprints.utp.edu.my/6432/2/Semantic_Based_Features_Selection_and_Weighting_method_for_text_classification.pdf Aurangzeb , khan and Baharum , Baharudin and Khairullah , khan (2010) Semantic Based Features Selection and Weighting Method for Text Classification. In: ITSIM'10, June 2010, Kuala Lumpur, Malaysia. http://eprints.utp.edu.my/6432/ |
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T Technology (General) Aurangzeb , khan Baharum , Baharudin Khairullah , khan Semantic Based Features Selection and Weighting Method for Text Classification |
description |
Feature selection and weighting is of vital concern in
text classification process which improves the efficiency and
accuracy of text classifier. Vector Space Model is used to
represent the documents using "Bag of Word" BOW model with
term weighting phenomena. Documents representation through
this model has some limitations that are, ignoring term
dependencies, structure and ordering of the terms in documents.
To overcome this problem, Semantics Base Feature Vector using
Part of Speech (POS), is proposed, which is used to extract the
concept of terms using WordNet, co-occurring and associated
terms. The proposed method is applied on small documents
dataset which shows that this method outperforms then term
frequency/ inverse document frequency (TF-IDF) with BOW
feature selection method for text classification. |
format |
Conference or Workshop Item |
author |
Aurangzeb , khan Baharum , Baharudin Khairullah , khan |
author_facet |
Aurangzeb , khan Baharum , Baharudin Khairullah , khan |
author_sort |
Aurangzeb , khan |
title |
Semantic Based Features Selection and Weighting
Method for Text Classification |
title_short |
Semantic Based Features Selection and Weighting
Method for Text Classification |
title_full |
Semantic Based Features Selection and Weighting
Method for Text Classification |
title_fullStr |
Semantic Based Features Selection and Weighting
Method for Text Classification |
title_full_unstemmed |
Semantic Based Features Selection and Weighting
Method for Text Classification |
title_sort |
semantic based features selection and weighting
method for text classification |
publishDate |
2010 |
url |
http://eprints.utp.edu.my/6432/1/Semantic_Based_Features_Selection_and_Weighting_method_for_text_classification.pdf http://eprints.utp.edu.my/6432/2/Semantic_Based_Features_Selection_and_Weighting_method_for_text_classification.pdf http://eprints.utp.edu.my/6432/ |
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1738655487131385856 |
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13.160551 |