Classification of Research Articles Hierarchically: A New Technique

The amount of research work taking place in all streams of Science, Engineering, Medicines, etc., is growing rapidly and hence the research articles are increasing everyday. In this dynamic environment, identifying and maintaining such a large collection of articles in one place and classifying them manually are becoming very exhaustive. Often, the allocation of articles in various subject areas will be made simply on the basis of the journals in which they are published. This paper proposes an approach for handling such huge volume of articles by classifying them into their respective categories based on the keywords extracted from the keyword section of the article. Query enrichment is used by generating unigram and bigram of these keywords and giving them proper weights using probability measure. Microsoft Academic Research dataset is used for the experimental purpose and the empirical results show the effectiveness of the propose approach.