We consider a novel setting for Named Entity Recognition (NER) where we have access to document-specific knowledge base tags. These tags consist of a canonical name from a knowledge base (KB) and entity type, but are not aligned to the text. We explore how to use KB tags to create document-specific gazetteers at inference time to improve NER. We find that this kind of supervision helps recognise organisations more than standard wide-coverage gazetteers. Moreover, augmenting document-specific gazetteers with KB information lets users specify fewer tags for the same performance, reducing cost.
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