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Weakly supervised parsing with rules

Abstract : This work proposes a new research direction to address the lack of structures in traditional n-gram models. It is based on a weakly supervised dependency parser that can model speech syntax without relying on any annotated training corpus. La- beled data is replaced by a few hand-crafted rules that encode basic syntactic knowledge. Bayesian inference then samples the rules, disambiguating and combining them to create complex tree structures that maximize a discriminative model's posterior on a target unlabeled corpus. This posterior encodes sparse se- lectional preferences between a head word and its dependents. The model is evaluated on English and Czech newspaper texts, and is then validated on French broadcast news transcriptions.
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Contributor : Christophe Cerisara <>
Submitted on : Friday, September 6, 2013 - 7:00:04 AM
Last modification on : Tuesday, September 24, 2019 - 4:00:09 PM
Long-term archiving on: : Saturday, December 7, 2013 - 4:14:25 AM


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  • HAL Id : hal-00850437, version 1



Christophe Cerisara, Alejandra Lorenzo, Pavel Kral. Weakly supervised parsing with rules. INTERSPEECH 2013, Aug 2013, Lyon, France. pp.2192-2196. ⟨hal-00850437⟩



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