Date of Original Version

6-2012

Type

Conference Proceeding

Journal Title

Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

First Page

577

Last Page

581

Rights Management

Copyright 2012 ACL

Abstract or Description

We investigate models for unsupervised learning with concave log-likelihood functions. We begin with the most well-known example, IBM Model 1 for word alignment (Brown et al., 1993) and analyze its properties, discussing why other models for unsupervised learning are so seldom concave. We then present concave models for dependency grammar induction and validate them experimentally. We find our concave models to be effective initializers for the dependency model of Klein and Manning (2004) and show that we can encode linguistic knowledge in them for improved performance.

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 of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 577-581.