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

Presented at Vancouver (Canada), 3-4 August 2017
PublisherStroudsburg PA : Association for Computational Linguistics
Publication date2017
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.

Citation (ISO format)
MOOR, Christophe et al. CLCL (Geneva) DINN Parser : a Neural Network Dependency Parser Ten Years Later. In: Proceedings of the CoNLL 2017 Shared Task : Multilingual Parsing from Raw Text to Universal Dependencies. Vancouver (Canada). Stroudsburg PA : Association for Computational Linguistics, 2017. p. 228–236. doi: 10.18653/v1/K17-3024
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