Information extraction (IE) is becoming increasingly useful as a form of shallow semantic analysis. Learning relational facts from text is one of the core tasks of IE and has applications in a variety of fields including summarization, question answering, and information retrieval. Previous work has traditionally relied on extensive human involvement (e.g., hand-annotated training instances, manual pattern extraction rules, hand-picked seeds). Standard supervised techniques can yield high performance when large amounts of hand-labeled data are available for a fixed inventory of relation types, however, extraction systems do not easily generalize beyond their training domains and often must be re-engineered for each application.
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