A Fast and Lightweight System for Multilingual Dependency Parsing

• Autores: Tao Ji, Yuanbin Wu, Man Lan
• 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. 237-242
• Idioma: inglés
• Enlaces
  • Texto Completo Libro (pdf)
• Resumen

  • Following Kiperwasser and Goldberg (2016), we present a multilingual dependency parser with a bidirectional LSTM (BiLSTM) feature extractor and a multi-layer perceptron (MLP) classifier.

    We trained our transition-based projective parser in UD version 2.0 datasets without any additional data. The parser is fast, lightweight and effective on big treebanks. In the CoNLL 2017 Shared Task: Multi- lingual Parsing from Raw Text to Universal Dependencies, the official results show that the macro-averaged LAS F1 score of our system Mengest is 61.33%.