Authors : M. Sathya , P.Venil and M.S. Saleem Basha
Abstract: Most of the information resources are capable to provide concept dependent results for the biased query. But the retrieval mechanism could not provide the relevant documents for the query text due to the size of the information resources is dynamically growing as the new topics being added. This problem can be overcome by automatically generating wrappers for these hidden documents. We are proposing a novel approach for automatically generating wrappers for describing the content of the hidden documents using a co-occurrence based clustering algorithm and Naive Bayesian classification model. The initial stage is the learning stage, which clusters the document based on the distinct concepts present in that. The learning technique makes use of a thesaurus and builds a co-occurrence correlation model. Then the clustered document features are used to generate the concept description using Naive Bayesian classifier. The join and posterior probabilities are calculated using the greedy selection and joining algorithm to represent cluster. Our implementation was tested on the standard data set and shows a better performance.
M. Sathya , P.Venil and M.S. Saleem Basha , 2007. A Novel Approach to Concept Extraction Using Naive Bayesian Classification Technique . International Journal of Soft Computing, 2: 488-493.