# On the Strength of Character Language Models for Multilingual Named Entity Recognition

arXiv: https://arxiv.org/abs/1809.05157 · HTML: https://mlsanity.com/paper/1809.05157/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2018.

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

- [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://mlsanity.com/paper/2103.06874/) (2021)
- [Protagonists' Tagger in Literary Domain -- New Datasets and a Method for Person Entity Linkage](https://mlsanity.com/paper/2110.01349/) (2021)
- [Does Character-level Information Always Improve DRS-based Semantic Parsing?](https://mlsanity.com/paper/2306.02302/) (2023)

## Abstract (arXiv)

Character-level patterns have been widely used as features in English Named Entity Recognition (NER) systems. However, to date there has been no direct investigation of the inherent differences between name and non-name tokens in text, nor whether this property holds across multiple languages. This paper analyzes the capabilities of corpus-agnostic Character-level Language Models (CLMs) in the binary task of distinguishing name tokens from non-name tokens. We demonstrate that CLMs provide a simple and powerful model for capturing these differences, identifying named entity tokens in a diverse set of languages at close to the performance of full NER systems. Moreover, by adding very simple CLM-based features we can significantly improve the performance of an off-the-shelf NER system for multiple languages.