Iranian Association for Management of Technology Journal of Science and Technology Policy 20080840 2 2 2009 06 22 A New Method for Intelligent Categorization of Scientific Texts (Case of Iran’s Nanotechnology Papers) A New Method for Intelligent Categorization of Scientific Texts (Case of Iran’s Nanotechnology Papers) 1 15 12777 FA Babak Teimourpour Mohammad Mehdi Sepehri - Leila Pezesh Journal Article 1970 01 01 Abstract: The ISI (Institute for Scientific Information) index is one of the most valuable and frequently used indicators for assessing indexed papers in science and technology journals. Categorization of these papers is a big challenge in management of technology. This paper introduces a new text categorization method - Silhouette based Unsupervised Text Categorization (SUTC). This method has been used for classifying Iranian nanotechnology papers indexed in ISI. First, a few standards are combined to make a comprehensive hierarchy of nanomaterials. Then, by applying information retrieval and text mining methods, papers are categorized intelligently without prior knowledge of class labels. The method is validated by comparing acquired class labels to the selected papers labeled by experts. Our analysis shows acceptable accuracy. Keywords: Scientometrics, Nanotechnology, Text mining, Text categorization, Clustering, Silhouette Coefficient Abstract: The ISI (Institute for Scientific Information) index is one of the most valuable and frequently used indicators for assessing indexed papers in science and technology journals. Categorization of these papers is a big challenge in management of technology. This paper introduces a new text categorization method - Silhouette based Unsupervised Text Categorization (SUTC). This method has been used for classifying Iranian nanotechnology papers indexed in ISI. First, a few standards are combined to make a comprehensive hierarchy of nanomaterials. Then, by applying information retrieval and text mining methods, papers are categorized intelligently without prior knowledge of class labels. The method is validated by comparing acquired class labels to the selected papers labeled by experts. Our analysis shows acceptable accuracy. Keywords: Scientometrics, Nanotechnology, Text mining, Text categorization, Clustering, Silhouette Coefficient https://jstp.nrisp.ac.ir/article_12777_efc1c4df94cbbf69d156d50ef328f54a.pdf