In this paper, we describe a technique to improve named entity recognition
in a resource-poor language (Hindi) by using cross-lingual information. We
use an on-line machine translation system and a separate word alignment phase
to find the projection of each Hindi word into the translated English sentence. We
estimate the cross-lingual features using an English named entity recognizer and
the alignment information. We use these cross-lingual features in a support vector
machine-based classifier. The use of cross-lingual features improves F1 score by
2.1 points absolute (2.9% relative) over a good-performing baseline model.