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 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.