Sequential graph dependency parser

Sean Welleck, Kyunghyun Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a method for non-projective dependency parsing by incrementally predicting a set of edges. Since the edges do not have a pre-specified order, we propose a set-based learning method. Our method blends graph, transition, and easy-first parsing, including a prior state of the parser as a special case. The proposed transition-based method successfully parses near the state of the art on both projective and non-projective languages, without assuming a certain parsing order.

Original languageEnglish (US)
Title of host publicationInternational Conference on Recent Advances in Natural Language Processing in a Deep Learning World, RANLP 2019 - Proceedings
EditorsGalia Angelova, Ruslan Mitkov, Ivelina Nikolova, Irina Temnikova, Irina Temnikova
PublisherIncoma Ltd
Pages1338-1345
Number of pages8
ISBN (Electronic)9789544520557
DOIs
StatePublished - Jan 1 2019
Event12th International Conference on Recent Advances in Natural Language Processing, RANLP 2019 - Varna, Bulgaria
Duration: Sep 2 2019Sep 4 2019

Publication series

NameInternational Conference Recent Advances in Natural Language Processing, RANLP
Volume2019-September
ISSN (Print)1313-8502

Conference

Conference12th International Conference on Recent Advances in Natural Language Processing, RANLP 2019
CountryBulgaria
CityVarna
Period9/2/199/4/19

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Cite this

Welleck, S., & Cho, K. (2019). Sequential graph dependency parser. In G. Angelova, R. Mitkov, I. Nikolova, I. Temnikova, & I. Temnikova (Eds.), International Conference on Recent Advances in Natural Language Processing in a Deep Learning World, RANLP 2019 - Proceedings (pp. 1338-1345). (International Conference Recent Advances in Natural Language Processing, RANLP; Vol. 2019-September). Incoma Ltd. https://doi.org/10.26615/978-954-452-056-4_153