Prédiction structurée pour l'analyse syntaxique en constituants par transitions : modèles denses et modèles creux

Maximin Coavoux, Benoît Crabbé

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

The article introduces a transition-based constituent parsing method relying on a deep-learning weighting scheme. This weighting method is compared to a structured perceptron, which is a more traditional method. First of all, we introduce a syntactic parser weighted by a local greedy neural network based on symbol embeddings. Then, we extend this model to a global beam-search based model. Our experiments highlight the surprisingly good properties of the greedy local neural parser.
Original languageFrench
Pages (from-to)59-83
Number of pages26
JournalTraitement Automatique des Langues
Issue number1
Publication statusPublished - 1 Oct 2016

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