A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing

• Autores: Dat Quoc Nguyen, Mark Dras, Mark Johnson
• Localización: Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies : August 3-4, 2017 Vancouver, Canada / Jan Hajic (ed. lit.), 2017, ISBN 978-1-945626-70-8, págs. 134-142
• Idioma: inglés
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• Resumen

  • We present a novel neural network model that learns POS tagging and graph-based dependency parsing jointly. Our model uses bidirectional LSTMs to learn feature representations shared for both POS tagging and dependency parsing tasks, thus handling the feature-engineering problem.

    Our extensive experiments, on 19 languages from the Universal Dependencies project, show that our model outperforms the state-of-the-art neural network-based Stack-propagation model for joint POS tagging and transition-based dependency parsing, resulting in a new state of the art.

    Our code is open-source and available together with pre-trained models at: https://github.com/ datquocnguyen/jPTDP .