Bootstrapped Text-level Named Entity Recognition for Literature

摘要

We present a named entity recognition (NER) system for tagging fiction: LitNER . Relative to more traditional approaches, LitNER has two important properties: (1) it makes no use of hand-tagged data or gazetteers, instead it boot-straps a model from term clusters; and (2) it leverages multiple instances of the same name in a text. Our experiments show it to substantially outperform off-the-shelf supervised NER systems.