Open AccessProceedings Article
Universal Dependency Annotation for Multilingual Parsing
Ryan McDonald,Joakim Nivre,Yvonne Quirmbach-Brundage,Yoav Goldberg,Dipanjan Das,Kuzman Ganchev,Keith Hall,Slav Petrov,Hao Zhang,Oscar Täckström,Claudia Bedini,Núria Bertomeu Castelló,Jungmee Lee +12 more
- Vol. 2, pp 92-97
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TLDR
A new collection of treebanks with homogeneous syntactic dependency annotation for six languages: German, English, Swedish, Spanish, French and Korean is presented, made freely available in order to facilitate research on multilingual dependency parsing.Abstract:
We present a new collection of treebanks with homogeneous syntactic dependency annotation for six languages: German, English, Swedish, Spanish, French and Korean. To show the usefulness of such a resource, we present a case study of crosslingual transfer parsing with more reliable evaluation than has been possible before. This ‘universal’ treebank is made freely available in order to facilitate research on multilingual dependency parsing. 1read more
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Proceedings Article
Universal Dependencies v1: A Multilingual Treebank Collection
Joakim Nivre,Marie-Catherine de Marneffe,Filip Ginter,Yoav Goldberg,Jan Hajič,Christopher D. Manning,Ryan McDonald,Slav Petrov,Sampo Pyysalo,Natalia Silveira,Reut Tsarfaty,Daniel Zeman +11 more
TL;DR: This paper describes v1 of the universal guidelines, the underlying design principles, and the currently available treebanks for 33 languages, as well as highlighting the needs for sound comparative evaluation and cross-lingual learning experiments.
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Universal Stanford dependencies: A cross-linguistic typology
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TL;DR: This work proposes a two-layered taxonomy: a set of broadly attested universal grammatical relations, to which language-specific relations can be added, and a lexicalist stance of the Stanford Dependencies, which leads to a particular, partially new treatment of compounding, prepositions, and morphology.
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Low Resource Dependency Parsing: Cross-lingual Parameter Sharing in a Neural Network Parser
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References
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Book
Dependency Parsing
TL;DR: This book surveys the three major classes of parsing models that are in current use: transition- based, graph-based, and grammar-based models, and gives a thorough introduction to the methods that are most widely used today.
Book ChapterDOI
The Prague Dependency Treebank
TL;DR: Inspired by the Penn Treebank, the most widely used syntactically annotated corpus of English, this work decided to develop a similarly sized corpus of Czech with a rich annotation scheme.
Journal ArticleDOI
Bootstrapping parsers via syntactic projection across parallel texts
TL;DR: Using parallel text to help solving the problem of creating syntactic annotation in more languages by annotating the English side of a parallel corpus, project the analysis to the second language, and train a stochastic analyzer on the resulting noisy annotations.
Proceedings Article
Transition-based Dependency Parsing with Rich Non-local Features
Yue Zhang,Joakim Nivre +1 more
TL;DR: This paper shows that it can improve the accuracy of transition-based dependency parsers by considering even richer feature sets than those employed in previous systems by improving the accuracy in the standard Penn Treebank setup and rivaling the best results overall.
Proceedings Article
Multi-Source Transfer of Delexicalized Dependency Parsers
TL;DR: This work demonstrates that delexicalized parsers can be directly transferred between languages, producing significantly higher accuracies than unsupervised parsers and shows that simple methods for introducing multiple source languages can significantly improve the overall quality of the resulting parsers.