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This paper describes our research on learning browsing behavior model for predicting the current information need of a web user. This inference is based on a parameterized model of how the sequence of browsing behavior indicates the degree to which page content satisfies the user's information need, and the model parameters can be estimated using standard methods from a labelled corpus. Data from lab experiments demonstrate that the prediction model can effectively identify the information needs of new users, browsing previously unseen pages. The paper concludes with an overview of our WebIC which integrates the model into a web browser, to help the user find the relevant information effectively from the web.