document: Unsupervised learning of soft patterns for generating definitions from online news Breaking news often contains timely definitions and descriptions of current terms , organizations and personalities . We utilize such web sources to construct definitions for such terms . Previous work has identified definitions using hand-crafted rules or supervised learning that constructs rigid , hard text patterns . In contrast , we demonstrate a new approach that uses flexible , soft matching patterns to characterize definition sentences . Our soft patterns are able to effectively accommodate the diversity of definition sentence structure exhibited in news . We use pseudo-relevance feedback to automatically label sentences for use in soft pattern generation . The application of our unsupervised method significantly improves baseline systems on both the standardized TREC corpus as well as crawled online news articles by 27 % and 30 % , respectively , in terms of F measure . When applied to a state-of-art definition generation system recently fielded in the TREC 2003 definitional question answering task , it improves the performance by 14 % .
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