@inproceedings{de-lhoneux-etal-2017-raw,
title = "From Raw Text to {U}niversal {D}ependencies - Look, No Tags!",
author = "de Lhoneux, Miryam and
Shao, Yan and
Basirat, Ali and
Kiperwasser, Eliyahu and
Stymne, Sara and
Goldberg, Yoav and
Nivre, Joakim",
editor = "Haji{\v{c}}, Jan and
Zeman, Dan",
booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K17-3022/",
doi = "10.18653/v1/K17-3022",
pages = "207--217",
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."
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%0 Conference Proceedings
%T From Raw Text to Universal Dependencies - Look, No Tags!
%A de Lhoneux, Miryam
%A Shao, Yan
%A Basirat, Ali
%A Kiperwasser, Eliyahu
%A Stymne, Sara
%A Goldberg, Yoav
%A Nivre, Joakim
%Y Hajič, Jan
%Y Zeman, Dan
%S Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F de-lhoneux-etal-2017-raw
%X 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.
%R 10.18653/v1/K17-3022
%U https://aclanthology.org/K17-3022/
%U https://doi.org/10.18653/v1/K17-3022
%P 207-217
Markdown (Informal)
[From Raw Text to Universal Dependencies - Look, No Tags!](https://aclanthology.org/K17-3022/) (de Lhoneux et al., CoNLL 2017)
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.