Abstract
Named entity recognition (NER) is widely used in natural language processing applications and downstream tasks. However, most NER tools target flat annotation from popular datasets, eschewing the semantic information available in nested entity mentions. We describe NNE-a fine-grained, nested named entity dataset over the full Wall Street Journal portion of the Penn Treebank (PTB). Our annotation comprises 279,795 mentions of 114 entity types with up to 6 layers of nesting. We hope the public release of this large dataset for English newswire will encourage development of new techniques for nested NER.
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CITATION STYLE
Ringland, N., Dai, X., Hachey, B., Karimi, S., Paris, C., & Curran, J. R. (2020). NNE: A dataset for nested named entity recognition in English newswire. In ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (pp. 5176–5181). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/p19-1510
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