  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.
