From Raw Text to Universal Dependencies - Look, No Tags!

Miryam de Lhoneux, Yan Shao, Ali Basirat, Eliyahu Kiperwasser, Sara Stymne, Yoav Goldberg, Joakim Nivre


Abstract
We present the Uppsala submission to the CoNLL 2017 shared task on parsing from raw text to universal dependencies. Our system is a simple pipeline consisting of two components. The first performs joint word and sentence segmentation on raw text; the second predicts dependency trees from raw words. The parser bypasses the need for part-of-speech tagging, but uses word embeddings based on universal tag distributions. We achieved a macro-averaged LAS F1 of 65.11 in the official test run, which improved to 70.49 after bug fixes. We obtained the 2nd best result for sentence segmentation with a score of 89.03.
Anthology ID:
K17-3022
Original:
K17-3022v1
Version 2:
K17-3022v2
Volume:
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Month:
August
Year:
2017
Address:
Vancouver, Canada
Editors:
Jan Hajič, Dan Zeman
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
207–217
Language:
URL:
https://aclanthology.org/K17-3022
DOI:
10.18653/v1/K17-3022
Bibkey:
Cite (ACL):
Miryam de Lhoneux, Yan Shao, Ali Basirat, Eliyahu Kiperwasser, Sara Stymne, Yoav Goldberg, and Joakim Nivre. 2017. From Raw Text to Universal Dependencies - Look, No Tags!. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 207–217, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
From Raw Text to Universal Dependencies - Look, No Tags! (de Lhoneux et al., CoNLL 2017)
Copy Citation:
PDF:
https://aclanthology.org/K17-3022.pdf
Poster:
 K17-3022.Poster.pdf
Data
Universal Dependencies