TAILIEUCHUNG - Automating the Construction of Internet Portals with Machine Learning

Domain-specic internet portals are growing in popularity because they gather content from the Web and organize it for easy access, retrieval and search. For example, allows complex queries by age, location, cost and specialty over summer camps. This functionality is not possible with general, Web-wide search engines. Unfortunately these portals are di cult and time-consuming to maintain. This paper advocates the use of machine learning techniques to greatly automate the creation and maintenance of domain-specic Internet portals. We describe new research in reinforcement learning, information extraction and text classication that enables e cient spidering, the identication of informative text segments, and the population of topic hierarchies. Using these techniques, we have. | Automating the Construction of Internet Portals with Machine Learning Andrew Kachites McCallum mccallum@ Just Research and Carnegie Mellon University Kamal Nigam knigam@ Carnegie Mellon University Jason Rennie jrennie@ Massachusetts Institute of Technology Kristie Seymore kseymore@ Carnegie Mellon University Abstract. Domain-specific internet portals are growing in popularity because they gather content from the Web and organize it for easy access retrieval and search. For example allows complex queries by age location cost and specialty over summer camps. This functionality is not possible with general Web-wide search engines. Unfortunately these portals are difficult and time-consuming to maintain. This paper advocates the use of machine learning techniques to greatly automate the creation and maintenance of domain-specific Internet portals. We describe new research in reinforcement learning information extraction and text classification that enables efficient spidering the identification of informative text segments and the population of topic hierarchies. Using these techniques we have built a demonstration system a portal for computer science research papers. It already contains over 50 000 papers and is publicly available at . These techniques are widely applicable to portal creation in other domains. Keywords spidering crawling reinforcement learning information extraction hidden Markov models text classification naive Bayes Expectation-Maximization unlabeled data 1. Introduction As the amount of information on the World Wide Web grows it becomes increasingly difficult to find just what we want. While generalpurpose search engines such as AltaVista and Google offer quite useful coverage it is often difficult to get high precision even for detailed queries. When we know that we want information of a certain type or on a certain topic a domain-specific Internet portal can be a

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