A Transition-based System for Universal Dependency Parsing

• Autores: Hao Wang, Hai Zhao, Zhisong Zhang
• 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. 191-197
• Idioma: inglés
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• Resumen

  • This paper describes the system for our participation of team Wanghao-ftd-SJTU in the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. In this work, we design a system based on UDPipe for universal dependency parsing, where transition based models are trained for different tree- banks. Our system directly takes raw texts as input, performing several intermediate steps like tokenizing and tagging, and finally generates the corresponding dependency trees. For the special surprise languages for this task, we adopt a delexicalized strategy and predict based on transfer learning from other related languages. In the final evaluation of the shared task, our system achieves a result of 66.53% in macro-averaged LAS F1-score.