Date of Original Version
Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
Copyright 2011 ACL
Abstract or Description
We describe a method for prediction of linguistic structure in a language for which only unlabeled data is available, using annotated data from a set of one or more helper languages. Our approach is based on a model that locally mixes between supervised models from the helper languages. Parallel data is not used, allowing the technique to be applied even in domains where human-translated texts are unavailable. We obtain state-of-theart performance for two tasks of structure prediction: unsupervised part-of-speech tagging and unsupervised dependency parsing.
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Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 50-61.