Abstract
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 handtagged data or gazetteers, instead it bootstraps 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.
Cite
CITATION STYLE
Brooke, J., Baldwin, T., & Hammond, A. (2016). Bootstrapped text-level named entity recognition for literature. In 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Short Papers (pp. 344–350). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/p16-2056
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