document: Combining linguistic and statistical analysis to extract relations from web documents The World Wide Web provides a nearly endless source of knowledge , which is mostly given in natural language . A first step towards exploiting this data automatically could be to extract pairs of a given semantic relation from text documents - for example all pairs of a person and her birthdate . One strategy for this task is to find text patterns that express the semantic relation , to generalize these patterns , and to apply them to a corpus to find new pairs . In this paper , we show that this approach profits significantly when deep linguistic structures are used instead of surface text patterns . We demonstrate how linguistic structures can be represented for machine learning , and we provide a theoretical analysis of the pattern matching approach . We show the benefits of our approach by extensive experiments with our prototype system LEILA .
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