@article{agic-etal-2016-multilingual,
title = "Multilingual Projection for Parsing Truly Low-Resource Languages",
author = "Agi{\'c}, {\v{Z}}eljko and
Johannsen, Anders and
Plank, Barbara and
Mart{\'\i}nez Alonso, H{\'e}ctor and
Schluter, Natalie and
S{\o}gaard, Anders",
editor = "Lee, Lillian and
Johnson, Mark and
Toutanova, Kristina",
journal = "Transactions of the Association for Computational Linguistics",
volume = "4",
year = "2016",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q16-1022",
doi = "10.1162/tacl_a_00100",
pages = "301--312",
abstract = "We propose a novel approach to cross-lingual part-of-speech tagging and dependency parsing for truly low-resource languages. Our annotation projection-based approach yields tagging and parsing models for over 100 languages. All that is needed are freely available parallel texts, and taggers and parsers for resource-rich languages. The empirical evaluation across 30 test languages shows that our method consistently provides top-level accuracies, close to established upper bounds, and outperforms several competitive baselines.",
}
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<abstract>We propose a novel approach to cross-lingual part-of-speech tagging and dependency parsing for truly low-resource languages. Our annotation projection-based approach yields tagging and parsing models for over 100 languages. All that is needed are freely available parallel texts, and taggers and parsers for resource-rich languages. The empirical evaluation across 30 test languages shows that our method consistently provides top-level accuracies, close to established upper bounds, and outperforms several competitive baselines.</abstract>
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%0 Journal Article
%T Multilingual Projection for Parsing Truly Low-Resource Languages
%A Agić, Željko
%A Johannsen, Anders
%A Plank, Barbara
%A Martínez Alonso, Héctor
%A Schluter, Natalie
%A Søgaard, Anders
%J Transactions of the Association for Computational Linguistics
%D 2016
%V 4
%I MIT Press
%C Cambridge, MA
%F agic-etal-2016-multilingual
%X We propose a novel approach to cross-lingual part-of-speech tagging and dependency parsing for truly low-resource languages. Our annotation projection-based approach yields tagging and parsing models for over 100 languages. All that is needed are freely available parallel texts, and taggers and parsers for resource-rich languages. The empirical evaluation across 30 test languages shows that our method consistently provides top-level accuracies, close to established upper bounds, and outperforms several competitive baselines.
%R 10.1162/tacl_a_00100
%U https://aclanthology.org/Q16-1022
%U https://doi.org/10.1162/tacl_a_00100
%P 301-312
Markdown (Informal)
[Multilingual Projection for Parsing Truly Low-Resource Languages](https://aclanthology.org/Q16-1022) (Agić et al., TACL 2016)
ACL