Universal Dependency Annotation for Multilingual Parsing

By Ryan T. McDonald, Joakim Nivre, Yvonne Quirmbach-Brundage, Yoav Goldberg, Dipanjan Das, Kuzman Ganchev, Keith B. Hall, Slav Petrov, Hao Zhang, Oscar Täckström and 3 more. 2013

603 citations recorded by the source corpus.

Subfields: Natural Language Processing, Computational Linguistics & Semantics

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Citations in the corpus

Universal Dependencies v1: A Multilingual Treebank Collection (2016, methodology): “It combines the (universal) Stanford dependencies (de Marneffe et al., 2006; Google dependency scheme (Universal Dependency Tree-banks) (McDonald et al., 2013), the Google universal part-of-speech tags (Petrov et al., 2012), and the Interset inter-lingua for morphosyntactic tag sets (Zeman, 2008)…”
CamemBERT: a Tasty French Language Model (2019, background): “For parsing, we observe error reductions in LAS of 2.96% for GSD, 3.33% for Sequoia, 1.70% for Spoken and 1.65% for Par-TUT.”
Exploiting Multi-typed Treebanks for Parsing with Deep Multi-task Learning (2016, methodology): “We discussed and veriﬁed this with the author of UDT v2.”
Structured Training for Neural Network Transition-Based Parsing (2015, background): “Lately, dependency parsing has emerged as a popular approach to this problem due to the availability of dependency tree-banks in many languages (Buchholz and Marsi, 2006; Nivre et al., 2007; McDonald et al., 2013) and the e ﬃ ciency of dependency parsers.”
IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP (2020, background): “The Universal Dependencies (UD) project 16 has released two different Indonesian corpora of relatively small size: (1) 5,593 sentences of UD-Indo-GSD (McDonald et al., 2013); 17 and (2) 1,000 sentences of UD-Indo-PUD (Zeman et al., 2018).”
A Universal Framework for Inductive Transfer Parsing across Multi-typed Treebanks (2016, methodology): “We investigate the effect of multilingual transfer parsing using the Universal Dependency Treebanks (UDT) (McDonald et al., 2013).”
The Inside-Outside Recursive Neural Network model for Dependency Parsing (2014, methodology): “In our experiments, we convert the Penn Treebank to dependencies using the Universal dependency annotation (McDonald et al., 2013) 2 ; this yields a dependency tree corpus we label PTB-U.”
Large-scale evidence of dependency length minimization in 37 languages (2015, methodology): “We draw data from three projects aimed at standardizing dependency annotation schemes and reformatting existing dependency corpora according to them: the Google Universal Dependency Treebank (23) (UDT), HamleDT 2.”
UDapter: Language Adaptation for Truly Universal Dependency Parsing (2020, background): “The cross-lingually consistent annotation efforts and the available treebanks in many languages (McDonald et al., 2013; Nivre et al., 2018) have provided an opportunity for cross-lingual studies to share information across languages within a single parser.”
Recent Advances in Google Real-Time HMM-Driven Unit Selection Synthesizer (2016, background): “dependency depth or dependency size to right lower bound) [8].”
Cross-lingual syntactic variation over age and gender (2015, methodology): “In our study, we are limited by the availability of comparable syntactically annotated corpora (McDonald et al., 2013) for ﬁve languages used in eleven countries, i.e., English (Australia, Canada, UK, and US), French (Belgium and France), German (Switzerland and Germany), Italian, Spanish, and…”
Universal Stanford dependencies: A cross-linguistic typology (2014, background): “0.0, used in the SANCL shared task, and Stanford Dependencies v3.”