NNE: A Dataset for Nested Named Entity Recognition in English Newswire
This provides a large, public dataset for English newswire to encourage development of new techniques for nested NER, addressing a gap in semantic information for NLP applications.
The authors tackled the lack of datasets for nested named entity recognition by creating NNE, a fine-grained dataset with 279,795 mentions of 114 entity types and up to 6 layers of nesting from the Wall Street Journal portion of the Penn Treebank.
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.