Given the large volumes of information that are generated every day in the Web, Adaptive Information Filtering systems have the potential to become a very useful tool to handle such information overload. These systems allow users to focus on documents that actually meet their information needs, while the system discards the rest. Traditionally, these systems assume that terms of a document are not related to each other, and therefore their efficacy is limited. To overcome this limitation, we propose a method for extracting different relations between the terms of the documents that satisfy the information needs of the users, in order to update the system modeling of such needs, and thereby improve its discrimination capability. These relations are based on the co-occurrence of terms at different levels of granularity, such as document, sentences or noun phrases. The experiments conducted indicate the potential of our proposal, which is capable of improving system efficacy, from the beginning and in the long run.
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