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PRAIg '16 / +pics

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9th international workshop on Machine Learning and Music

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Riva del Garda (Italy): 19th - 23rd September 2016

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  1. Verdú-Mas, J.L.; Carrasco, R.C.; Calera-Rubio, J.
    "Parsing with probabilistic strictly locally testable tree languages"
    IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, pp. 1040-1050 (2005)
    : bibtex
    Abstract:

    Probabilistic k-testable models (usually known as k-gram models in the case of strings) can be easily identified from samples and allow for smoothing techniques to deal with unseen events during pattern classification. In this paper, we introduce the family of stochastic k-testable tree languages and describe how these models can approximate any stochastic rational tree language. The model is applied to the task of learning a probabilistic k-testable model from a sample of parsed sentences. In particular, a parser for a natural language grammar that incorporates smoothing is shown.

@article {
 author = "Verdú-Mas, J.L.; Carrasco, R.C.; Calera-Rubio, J.",
 title  = "Parsing with probabilistic strictly locally testable tree languages",
 journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
 number = "7",
 pages = "1040-1050",
 volume = "27",
 year = "2005"
}
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