Antoine Rozenknop


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Representation Learning and Dynamic Programming for Arc-Hybrid Parsing
Joseph Le Roux | Antoine Rozenknop | Mathieu Lacroix
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

We present a new method for transition-based parsing where a solution is a pair made of a dependency tree and a derivation graph describing the construction of the former. From this representation we are able to derive an efficient parsing algorithm and design a neural network that learns vertex representations and arc scores. Experimentally, although we only train via local classifiers, our approach improves over previous arc-hybrid systems and reach state-of-the-art parsing accuracy.


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Dependency Parsing with Bounded Block Degree and Well-nestedness via Lagrangian Relaxation and Branch-and-Bound
Caio Corro | Joseph Le Roux | Mathieu Lacroix | Antoine Rozenknop | Roberto Wolfler Calvo
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Syntactic Parsing and Compound Recognition via Dual Decomposition: Application to French
Joseph Le Roux | Antoine Rozenknop | Matthieu Constant
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers


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Combining PCFG-LA Models with Dual Decomposition: A Case Study with Function Labels and Binarization
Joseph Le Roux | Antoine Rozenknop | Jennifer Foster
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing


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Speech Recognition Simulation and its Application for Wizard-of-Oz Experiments
Alex Trutnev | Antoine Rozenknop | Martin Rajman
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)