@inproceedings{9023ce0d269e442999d739cbe28e8491,
title = "From raw text to universal dependencies – Look, no tags!",
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 and obtained the 2nd best result for sentence segmentation with a score of 89.03. After fixing two bugs, we obtained an unofficial LAS F1 of 70.49.",
author = "{de Lhoneux}, Miryam and Yan Shao and Ali Basirat and Eliyahu Kiperwasser and Sara Stymne and Yoav Goldberg and Joakim Nivre",
note = "Publisher Copyright: {\textcopyright} 2017 Association for Computational Linguistics.; 2017 SIGNLL Conference on Computational Natural Language Learning- CoNLL Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, CoNLL 2017 ; Conference date: 03-08-2017 Through 04-08-2017",
year = "2017",
month = jan,
day = "1",
doi = "10.18653/v1/k17-3022",
language = "American English",
series = "CoNLL 2017 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
publisher = "Association for Computational Linguistics (ACL)",
pages = "207--217",
booktitle = "CoNLL 2017 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2017 Shared Task",
address = "United States",
}