Discovering meta-information from collections of text documents is becoming an important research area due to increasing demands for automated analysis of large text collections. This analysis process usually involves first structuring the unstructured text data and then deriving useful patterns from this structured text data. This process involves concepts taken from multiple fields such as data mining, artificial intelligence, statistics, databases, and linguistics. There are several focus areas within this research domain, each pursuing somewhat different objectives. Some of these focus areas include: information retrieval, document clustering, document classification, web mining, information extraction, natural language processing, and concept extraction. In this dissertation we have developed several novel methodologies to discover information embedded in text document collections and have presented solutions to some specific pattern discovery tasks.