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

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

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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://preview.aclanthology.org/teach-a-man-to-fish/K17-3022.pdf
Poster:
 K17-3022.Poster.pdf
Data
Universal Dependencies
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