Using BiLSTM in Dependency Parsing for Vietnamese

Authors

  • Luong Nguyen Thi Dalat University, Lamdong, Vietnam
  • Linh Ha My VNU University of Science, Hanoi, Vietnam
  • Huyen Nguyen Thi Minh VNU University of Science, Hanoi, Vietnam
  • Phuong Le-Hong VNU University of Science, Hanoi, Vietnam

DOI:

https://doi.org/10.13053/cys-22-3-3023

Abstract

Recently, deep learning methods have achieved good results in dependency parsing for many natural languages. In this paper, we investigate the use of bidirectional long short-term memory network models for both transition-based and graph-based dependency parsing for the Vietnamese language. We also reportour contribution in building a Vietnamese dependency treebank whose tagset conforms to the Universal Dependency schema. Various experiments demonstrate the efficiency of this method, which achieves the best parsing accuracy in comparison to other existing approaches on the same corpus, with unlabeled attachment score of 84.45% or labeled attachment scoreof 78.56%.

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Published

2018-09-25

Issue

Vol. 22 No. 3 (2018): Thematic issue on Natural Language Processing (Guest Editor: A. Gelbukh)

Section

Articles of the Thematic Issue

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