Joint POS Tagging and Dependency Parsing with Transition-based Neural Networks

نویسندگان

  • Liner Yang
  • Meishan Zhang
  • Yang Liu
  • Nan Yu
  • Maosong Sun
  • Guohong Fu
چکیده

While part-of-speech (POS) tagging and dependency parsing are observed to be closely related, existing work on joint modeling with manually crafted feature templates suffers from the feature sparsity and incompleteness problems. In this paper, we propose an approach to joint POS tagging and dependency parsing using transitionbased neural networks. Three neural network based classifiers are designed to resolve shift/reduce, tagging, and labeling conflicts. Experiments show that our approach significantly outperforms previous methods for joint POS tagging and dependency parsing across a variety of natural languages.

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عنوان ژورنال:
  • CoRR

دوره abs/1704.07616  شماره 

صفحات  -

تاریخ انتشار 2017