# Neural Reranking for Named Entity Recognition

arXiv: https://arxiv.org/abs/1707.05127 · HTML: https://mlsanity.com/paper/1707.05127/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2017.

## Built on by (most cited first, 4 of 4)

- [Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training](https://mlsanity.com/paper/2210.05287/) (2022)
- [TURNER: The Uncertainty-based Retrieval Framework for Chinese NER](https://mlsanity.com/paper/2202.09022/) (2022)
- [YATO: Yet Another deep learning based Text analysis Open toolkit](https://mlsanity.com/paper/2209.13877/) (2022)
- [KARL-Trans-NER: Knowledge Aware Representation Learning for Named Entity Recognition using Transformers](https://mlsanity.com/paper/2111.15436/) (2021)

## Abstract (arXiv)

We propose a neural reranking system for named entity recognition (NER). The basic idea is to leverage recurrent neural network models to learn sentence-level patterns that involve named entity mentions. In particular, given an output sentence produced by a baseline NER model, we replace all entity mentions, such as textitBarack Obama, into their entity types, such as textitPER. The resulting sentence patterns contain direct output information, yet is less sparse without specific named entities. For example, "PER was born in LOC" can be such a pattern. LSTM and CNN structures are utilised for learning deep representations of such sentences for reranking. Results show that our system can significantly improve the NER accuracies over two different baselines, giving the best reported results on a standard benchmark.