An Investigation into the Process of Organizing and Retrieving Web Texts Based on the Integration of Semantic Concepts In order to organize knowledge

Abstract

Improvement in information retrieval performance relates to the method of knowledge extraction from large amounts of text information on web. Text classification is one of application of knowledge extraction with supervised machine learning methods. This paper proposed Kullback-Leibler divergence KNN for classifying extracted features based on term weighting with Latent Dirichlet Allocation Algorithm. LDA is Non Negative matrix factorization method proposed for topic modelling and dimension reduction of high dimensional feature space .In traditional LDA, each component value is assigned using the information retrieval TF measure, While this weighting method seems very appropriate for IR, it is not clear that it is the best choice for TC problems. Actually, this weighting method does not leverage the information implicitly contained in the categorization task to represent documents. In this paper, we introduce a new weighting method based on Point wise Mutual Information for accessing the importance of a word for a specific latent concept, then each document classified based on probability distribution over the latent topics. Experimental result investigated when we used PMI measure for term Weighing and KNN with Kullback-Leibler distance, accuracy has been 82.5%, with lower complexity and same accuracy versus complex deep learning methods.

Keywords

• text mining
• text classification
• topic modeling
• latent dirichlet allocation
• document representation
• knowledge organization
• pointwise mutual information.