  Although over 100 languages are supported by strong off-the-shelf machine
translation systems, only a subset of them possess large annotated corpora for
named entity recognition. Motivated by this fact, we leverage machine
translation to improve annotation-projection approaches to cross-lingual named
entity recognition. We propose a system that improves over prior
entity-projection methods by: (a) leveraging machine translation systems twice:
first for translating sentences and subsequently for translating entities; (b)
matching entities based on orthographic and phonetic similarity; and (c)
identifying matches based on distributional statistics derived from the
dataset. Our approach improves upon current state-of-the-art methods for
cross-lingual named entity recognition on 5 diverse languages by an average of
4.1 points. Further, our method achieves state-of-the-art F_1 scores for
Armenian, outperforming even a monolingual model trained on Armenian source
data.
