Abstract
Recent approaches for building syntactic language models include the combination of Probabilistic Tree Substitution Grammars (PTSGs) and Bayesian learning methods. While PTSGs have appealing features for syntax modeling, Bayesian methods provide a framework for inducing compact grammars that do not overfit the training corpus. In this paper, we apply these approaches to learn syntactic language models from a Brazilian Portuguese treebank.
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Beck, D.E., de Medeiros Caseli, H. (2012). Bayesian Induction of Syntactic Language Models for Brazilian Portuguese. In: Caseli, H., Villavicencio, A., Teixeira, A., Perdigão, F. (eds) Computational Processing of the Portuguese Language. PROPOR 2012. Lecture Notes in Computer Science(), vol 7243. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28885-2_18
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DOI: https://doi.org/10.1007/978-3-642-28885-2_18
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