@inproceedings{shi-etal-2017-combining,
title = "Combining Global Models for Parsing {U}niversal {D}ependencies",
author = "Shi, Tianze and
Wu, Felix G. and
Chen, Xilun and
Cheng, Yao",
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/K17-3003",
doi = "10.18653/v1/K17-3003",
pages = "31--39",
abstract = "We describe our entry, C2L2, to the CoNLL 2017 shared task on parsing Universal Dependencies from raw text. Our system features an ensemble of three global parsing paradigms, one graph-based and two transition-based. Each model leverages character-level bi-directional LSTMs as lexical feature extractors to encode morphological information. Though relying on baseline tokenizers and focusing only on parsing, our system ranked second in the official end-to-end evaluation with a macro-average of 75.00 LAS F1 score over 81 test treebanks. In addition, we had the top average performance on the four surprise languages and on the small treebank subset.",
}
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%0 Conference Proceedings
%T Combining Global Models for Parsing Universal Dependencies
%A Shi, Tianze
%A Wu, Felix G.
%A Chen, Xilun
%A Cheng, Yao
%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 shi-etal-2017-combining
%X We describe our entry, C2L2, to the CoNLL 2017 shared task on parsing Universal Dependencies from raw text. Our system features an ensemble of three global parsing paradigms, one graph-based and two transition-based. Each model leverages character-level bi-directional LSTMs as lexical feature extractors to encode morphological information. Though relying on baseline tokenizers and focusing only on parsing, our system ranked second in the official end-to-end evaluation with a macro-average of 75.00 LAS F1 score over 81 test treebanks. In addition, we had the top average performance on the four surprise languages and on the small treebank subset.
%R 10.18653/v1/K17-3003
%U https://aclanthology.org/K17-3003
%U https://doi.org/10.18653/v1/K17-3003
%P 31-39
Markdown (Informal)
[Combining Global Models for Parsing Universal Dependencies](https://aclanthology.org/K17-3003) (Shi et al., CoNLL 2017)
ACL
- Tianze Shi, Felix G. Wu, Xilun Chen, and Yao Cheng. 2017. Combining Global Models for Parsing Universal Dependencies. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 31–39, Vancouver, Canada. Association for Computational Linguistics.