Boosting for Efficient Model Selection for Syntactic Parsing

Abstract : We present an efficient model selection method using boosting for transition-based constituency parsing. It is designed for exploring a high-dimensional search space, defined by a large set of feature templates, as for example is typically the case when parsing morphologically rich languages. Our method removes the need to manually define heuristic constraints, which are often imposed in current state-of-the-art selection methods. Our experiments for French show that the method is more efficient and is also capable of producing compact, state-of-the-art models.
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Communication dans un congrès
COLING 2016 - 26th International Conference on Computational Linguistics, Dec 2016, Osaka, Japan. pp.1-11, 2016
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Rachel Bawden, Benoît Crabbé. Boosting for Efficient Model Selection for Syntactic Parsing. COLING 2016 - 26th International Conference on Computational Linguistics, Dec 2016, Osaka, Japan. pp.1-11, 2016. 〈hal-01391743v2〉

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