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.