# Merge and Label: A novel neural network architecture for nested NER

## TL;DR

This paper introduces a neural network architecture for nested NER that merges tokens into nested structures and labels them independently, achieving state-of-the-art results on ACE 2005 with improved F1 scores.

## Contribution

The novel merge and label neural network architecture effectively handles nested NER structures and outperforms previous methods on benchmark datasets.

## Key findings

- Achieves 74.6 F1 on ACE 2005
- Improves to 82.4 F1 with BERT embeddings
- Maintains performance on flat NER tasks

## Abstract

Named entity recognition (NER) is one of the best studied tasks in natural language processing. However, most approaches are not capable of handling nested structures which are common in many applications. In this paper we introduce a novel neural network architecture that first merges tokens and/or entities into entities forming nested structures, and then labels each of them independently. Unlike previous work, our merge and label approach predicts real-valued instead of discrete segmentation structures, which allow it to combine word and nested entity embeddings while maintaining differentiability. %which smoothly groups entities into single vectors across multiple levels. We evaluate our approach using the ACE 2005 Corpus, where it achieves state-of-the-art F1 of 74.6, further improved with contextual embeddings (BERT) to 82.4, an overall improvement of close to 8 F1 points over previous approaches trained on the same data. Additionally we compare it against BiLSTM-CRFs, the dominant approach for flat NER structures, demonstrating that its ability to predict nested structures does not impact performance in simpler cases.