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Using Universal Linguistic Knowledge to Guide Grammar Induction

Author(s)
Naseem, Tahira; Chen, Harr; Barzilay, Regina; Johnson, Mark
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Abstract
We present an approach to grammar induction that utilizes syntactic universals to improve dependency parsing across a range of languages. Our method uses a single set of manually-specified language-independent rules that identify syntactic dependencies between pairs of syntactic categories that commonly occur across languages. During inference of the probabilistic model, we use posterior expectation constraints to require that a minimum proportion of the dependencies we infer be instances of these rules. We also automatically refine the syntactic categories given in our coarsely tagged input. Across six languages our approach outperforms state-of-the-art unsupervised methods by a significant margin.
Description
URL to papers list on conference site
Date issued
2010-10
URI
http://hdl.handle.net/1721.1/63155
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
Proceedings of EMNLP 2010: Conference on Empirical Methods in Natural Language Processing
Citation
Naseem, Tahira et al. "Using Universal Linguistic Knowledge to Guide Grammar Induction." Proceedings of EMNLP 2010: Conference on Empirical Methods in Natural Language Processing, October 9-11, 2010, MIT, Massachusetts, USA.
Version: Author's final manuscript

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