Date of Original Version

7-2011

Type

Article

Journal Title

Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)

First Page

50

Last Page

61

Rights Management

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.

Creative Commons License

Creative Commons Attribution-Noncommercial-Share Alike 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

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Published In

Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 50-61.