Abstract
This paper describes a hybrid language model defined as a combination of a word-based n-gram, which is used to capture the local relations between words, and a category-based SCFG with a word distribution into categories, which is defined to represent the long-term relations between these categories. Experiments on the UPenn Treebank corpus are reported. These experiments have been carried out in terms of the test set perplexity and the word error rate in a speech recognition experiment.
This work has been partially supported by the Spanish CICYT under contract (TIC2002/04103-C03-03)
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García-Hernandez, J., Sánchez, J.A., Benedí, J.M. (2003). Performance and Improvements of a~Language Model Based on Stochastic Context-Free Grammars. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_32
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DOI: https://doi.org/10.1007/978-3-540-44871-6_32
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