Learning dictionaries for information extraction by multi-level bootstrapping
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Discovering asymmetric entailment relations between verbs using selectional preferences
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Structured relation discovery using generative models
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A challenging problem in open information extraction and text mining is the learning of the selectional restrictions of semantic relations. We propose a minimally supervised bootstrapping algorithm that uses a single seed and a recursive lexico-syntactic pattern to learn the arguments and the supertypes of a diverse set of semantic relations from the Web. We evaluate the performance of our algorithm on multiple semantic relations expressed using "verb", "noun", and "verb prep" lexico-syntactic patterns. Human-based evaluation shows that the accuracy of the harvested information is about 90%. We also compare our results with existing knowledge base to outline the similarities and differences of the granularity and diversity of the harvested knowledge.