A Minimal Span-Based Neural Constituency Parser

@article{Stern2017AMS,
  title={A Minimal Span-Based Neural Constituency Parser},
  author={Mitchell Stern and Jacob Andreas and D. Klein},
  journal={ArXiv},
  year={2017},
  volume={abs/1705.03919}
}
  • Mitchell Stern, Jacob Andreas, D. Klein
  • Published 2017
  • Computer Science
  • ArXiv
  • In this work, we present a minimal neural model for constituency parsing based on independent scoring of labels and spans. [...] Key Result We demonstrate empirically that both prediction schemes are competitive with recent work, and when combined with basic extensions to the scoring model are capable of achieving state-of-the-art single-model performance on the Penn Treebank (91.79 F1) and strong performance on the French Treebank (82.23 F1).Expand Abstract
    Efficient Constituency Parsing by Pointing
    A Rich-label Constituency Tree for Constituency Parsing
    Straight to the Tree: Constituency Parsing with Neural Syntactic Distance
    32
    Two Local Models for Neural Constituent Parsing
    9
    Unlexicalized Transition-based Discontinuous Constituency Parsing
    8
    Effective Inference for Generative Neural Parsing
    27
    Accurate SHRG-Based Semantic Parsing
    16
    Span-Based LCFRS-2 Parsing
    1

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