# Improving Neural Named Entity Recognition with Gazetteers

arXiv: https://arxiv.org/abs/2003.03072 · HTML: https://mlsanity.com/paper/2003.03072/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [Neural Entity Recognition with Gazetteer based Fusion](https://mlsanity.com/paper/2105.13225/) (2021)
- [KILDST: Effective Knowledge-Integrated Learning for Dialogue State Tracking using Gazetteer and Speaker Information](https://mlsanity.com/paper/2301.07341/) (2023)
- [Incorporating Dictionaries into a Neural Network Architecture to Extract COVID-19 Medical Concepts From Social Media](https://mlsanity.com/paper/2309.02188/) (2023)

## Abstract (arXiv)

The goal of this work is to improve the performance of a neural named entity recognition system by adding input features that indicate a word is part of a name included in a gazetteer. This article describes how to generate gazetteers from the Wikidata knowledge graph as well as how to integrate the information into a neural NER system. Experiments reveal that the approach yields performance gains in two distinct languages: a high-resource, word-based language, English and a high-resource, character-based language, Chinese. Experiments were also performed in a low-resource language, Russian on a newly annotated Russian NER corpus from Reddit tagged with four core types and twelve extended types. This article reports a baseline score. It is a longer version of a paper in the 33rd FLAIRS conference (Song et al. 2020).