  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.
