# NNE: A Dataset for Nested Named Entity Recognition in English Newswire

## TL;DR

This paper introduces NNE, a comprehensive nested named entity dataset for English newswire, designed to facilitate the development of advanced nested NER techniques by providing detailed multi-layered annotations.

## Contribution

The paper presents a large, fine-grained nested NER dataset with extensive annotations, addressing the lack of resources for nested entity recognition in English newswire.

## Key findings

- Contains 279,795 mentions of 114 entity types
- Supports up to 6 layers of nesting
- Aims to promote new nested NER methods

## 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.