Abstract

In this thesis, we investigate the problem of identifying, within a text, relations that capture information important for event-focused document collections. The presented solutions work with events of various granularity and we show how to use these relations to improve the performance of a number of natural language processing applications. For a set of related event-focused documents, we introduce a notion of a shallow semantic network based on the relations between the important elements discovered in these documents. This shallow semantic network captures the most important relations among the objects, people, and other elements that are involved in the events described in the input document collection. We present experimental evidence that such a relation-based representation of event-focused documents is superior to techniques that rely on term frequencies for the task of information selection. For a set

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