  Named Entities (NEs) are often written with no orthographic changes across
different languages that share a common alphabet. We show that this can be
leveraged so as to improve named entity recognition (NER) by using unsupervised
word clusters from secondary languages as features in state-of-the-art
discriminative NER systems. We observe significant increases in performance,
finding that person and location identification is particularly improved, and
that phylogenetically close languages provide more valuable features than more
distant languages.
