CLCL (Geneva) DINN Parser: a Neural Network Dependency Parser Ten Years Later

Christophe Moor, Paola Merlo, James Henderson, Haozhou Wang


Abstract
This paper describes the University of Geneva’s submission to the CoNLL 2017 shared task Multilingual Parsing from Raw Text to Universal Dependencies (listed as the CLCL (Geneva) entry). Our submitted parsing system is the grandchild of the first transition-based neural network dependency parser, which was the University of Geneva’s entry in the CoNLL 2007 multilingual dependency parsing shared task, with some improvements to speed and portability. These results provide a baseline for investigating how far we have come in the past ten years of work on neural network dependency parsing.
Anthology ID:
K17-3024
Volume:
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
228–236
Language:
URL:
https://www.aclweb.org/anthology/K17-3024
DOI:
10.18653/v1/K17-3024
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PDF:
http://aclanthology.lst.uni-saarland.de/K17-3024.pdf