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Annotating Resources for Information Extraction
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Annotating Resources for Information Extraction
Trained systems for NE extraction have shown significant promise because of their robustness to errorful input and rapid adaptability. However, these learning algorithms have transferred the cost of development from skilled computational linguistic expertise to data annotation, putting a new premium on effective ways to produce high-quality annotated resources at minimal cost. The paper reflects on BBN’s four years of experience in the annotation of training data for Named Entity (NE) extraction systems discussing useful techniques for maximizing data quality and quantity.
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