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arXiv · 1705.05952

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

Dat Quoc Nguyen·Mark Dras·Mark Johnson
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Abstract

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

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Dat Quoc Nguyen, Mark Dras, Mark Johnson. 2017-06-08. A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing. https://doi.org/10.18653/v1%2Fk17-3014

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