A Novel Method for Efficient Multi-Label Text Categorization of research articles

An important research field in the area of text mining is text categorization. Most of the real world documents are multi-label in nature. In this paper we have proposed a novel method for automated and effective categorization of multi-label text documents. The proposed method is based on lexical and semantics concepts. Tokens are identified in the text documents using standard IEEE taxonomy. To analyze the semantic relationships between tokens, standard lexical database WordNet is used. The proposed method is tested on a dataset of 150 research articles of computer science domain from IEEE Xplore digital library. It has shown a significantly good performance with an accuracy of 75%.