Abstract- Information exists in every aspect of our life. The expansion of the web has helped to this direction. The web feeds us with enormous information and the widespread use of computers and other hardware appliances has lead us to a state where we have a lot of information in our hands, but many times it is useless. People are not able to find information that they really need but already own. How many times have you tried to find a specific article that you have, or a specific mail that you received, or even an SMS from someone saying something specific. For this reason many information retrieval techniques have been proposed and many information extraction mechanisms have been created. In this paper we will provide the experimental evaluation of a keyword extraction mechanism and how we treat different types of text (news articles, publications, e-mails). This keyword extraction mechanism is a part of a complete system that includes information retrieval, information extraction, categorization and publication of information to a personalized portal.

This disseration is devoted to investigate the method for building a high-quality homepage collection from the web efficiently by considering the page group struc-tures. We mainly investigate in researchers ’ homepages and homepages of other categories partly. A web page collection with a guaranteed high quality (i.e., high recall and high precision) is required for implementing high quality web-based information services. Building such a collection demands a large amount of human work, however, be-cause of the diversity, vastness and sparseness of web pages. Even though many researchers have investigated methods for searching and classifying web pages, etc., most of the methods are best-effort types and pay no attention to quality assurance. We are therefore investigating a method for building a homepage collection effi-ciently while assuring a given high quality, with the expectation that the investigated method can be applicable to the collection of various categories of homepages. This dissertation consists of seven chapters. Chapter 1 gives the introduction,