Existing mechanisms for concept discovery tend to pick up all possible relationships between terms in a document based on roles of terms identified [3]. The proposed work aims to enhance this discovery process by employing machine learning and semantic modelling. We explore a framework for automatically discovering labeled clusters from a large collection of documents. The aim of this framework is to enable the extraction of concepts and to structure these into labeled concepts for use by text processing applications such as text summarization and text categorization. We have developed a mechanism for automatically inducing a set of words that captures the meaning of a collection of documents. The WordNet lexical database is used to extract root meanings and to determine relationships amongst these terms.