On statistical parsing of French with supervised and semi-supervised strategies

Abstract : This paper reports preliminary results on grammatical induction for French. We investigate how to best train a parser on the French Treebank (Abeillé and Barrier, 2004), viewing the task as a trade-off between generalizability and interpretability. We compare on French a supervised lexicalized parsing algorithm with a semi-supervised unlexicalized algorithm Petrov et al. (2006) along the lines of Crabbé and Candito (2008). We report the best results known to us on French statistical parsing with the semi-supervised learning algorithm, and the reported experiments can give insights for the task of grammatical learning for a morphologically-rich language, with a relatively limited amount of training data, annotated with a rather flat structure.
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Communication dans un congrès
Association for Computational Linguistics. EACL 2009 workshop on Computational Linguistic Aspects of Grammatical Inference, Mar 2009, Athens, Greece. pp.49-57, 2009
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  • HAL Id : hal-00495290, version 1

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Marie Candito, Benoît Crabbé, Djamé Seddah. On statistical parsing of French with supervised and semi-supervised strategies. Association for Computational Linguistics. EACL 2009 workshop on Computational Linguistic Aspects of Grammatical Inference, Mar 2009, Athens, Greece. pp.49-57, 2009. 〈hal-00495290〉

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