Some recent approaches: Cross & Huang (2016)[1], Kitaev & Klein (2020)[2]

Difficulties Edit

Proper names Edit

Proper names are typically out-of-vocabulary due to their diversity. This is potentially a problem but when Jelínek (2015)[3] simpilfies proper names (along with other 79 groups of words), they've got an improvement of only less than 1% (UAS: 86.12% --> 86.44%, LAS: 79.86% --> 80.58%).

References Edit

  1. Cross, J., & Huang, L. (2016). Span-based constituency parsing with a structure-label system and provably optimal dynamic oracles. EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings, (Section 2), 1–11.
  2. Kitaev, N., & Klein, D. (2020). Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference, 6255–6261.
  3. Jelínek, Tomás. "Improvements to Dependency Parsing Using Automatic Simplification of Data." In LREC, vol. 14, pp. 73-77. 2014.
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