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acquisition

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Estimating Grammar Parameters Using Bounded Memorying approaches make repeated passes over the training data. The memory requirements of such algorithms are ill-suited for embedded agents exposed to large amounts of training data over long periods of time. We present a novel algorithm, called HOLA, for estimating the parameters of SCFGs that comput

exacerbate

Stochastic ,-testable Tree Languages and Applicationslass of the languages recognizable by ascending tree automata. One of the advantages of this approach is that the probabilistic model can be updated in an incremental fashion. Another feature is that backing-off schemes can be defined. As an illustration of their applicability, they have been used t

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PHONE

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https://doi.org/10.1007/978-3-319-59318-0r noun phrase extraction, a task that is crucial in many applications involving natural language processing. In the experiments, Partition Search improves parsing performance by up to 21.45% compared to a general baseline and by up to 3.48% compared to a task-specific baseline, while reducing grammar size by up to 17.25%.

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