The objective of any tutoring system is to provide a meaningful learning to the learner. Therefore an automated tutoring system should be able to know whether a concept mentioned in a document is a prerequisite for studying that document, or it can be learned from it. This paper addresses the problem of identifying defined concepts and prerequisite concepts from learning resources in html format. In this paper a supervised machine learning approach was taken to address the problem, based on linguistic features which enclose contextual information and stylistic features such as font size and font weight. This paper shows that contextual information in addition to format information can give better results when used with the SVM classifier than with the (LP)2 algorithm.