NNE: A Dataset for Nested Named Entity Recognition in English Newswire
Nicky Ringland, Xiang Dai, Ben Hachey, Sarvnaz Karimi, Cecile Paris, James R. Curran
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.- Anthology ID:
- P19-1510
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5176–5181
- Language:
- URL:
- https://aclanthology.org/P19-1510
- DOI:
- 10.18653/v1/P19-1510
- Cite (ACL):
- Nicky Ringland, Xiang Dai, Ben Hachey, Sarvnaz Karimi, Cecile Paris, and James R. Curran. 2019. NNE: A Dataset for Nested Named Entity Recognition in English Newswire. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5176–5181, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- NNE: A Dataset for Nested Named Entity Recognition in English Newswire (Ringland et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/P19-1510.pdf
- Data
- NNE, GENIA