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Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection

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arxiv 2004.10643 v1 pith:GF7NAYAE submitted 2020-04-22 cs.CL

Joakim Nivre , Marie-Catherine de Marneffe , Filip Ginter , Jan Hajič , Christopher D. Manning , Sampo Pyysalo , Sebastian Schuster , Francis Tyers
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Daniel Zeman
This is my paper · ORCID
classification cs.CL
keywords universalannotationdependencieslanguageslayermorphologicalsyntactictreebank
verification ladder T0 review T1 audit T2 compute T3 formal

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Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework. The annotation consists in a linguistically motivated word segmentation; a morphological layer comprising lemmas, universal part-of-speech tags, and standardized morphological features; and a syntactic layer focusing on syntactic relations between predicates, arguments and modifiers. In this paper, we describe version 2 of the guidelines (UD v2), discuss the major changes from UD v1 to UD v2, and give an overview of the currently available treebanks for 90 languages.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Segment First or Comprehend First? Explore the Limit of Unsupervised Word Segmentation with Large Language Models

    cs.CL 2025-05 conditional novelty 6.0 of 10

    LLMs can segment boundary-less languages directly from prompts, and the LLACA method distills this ability into a fast automaton that beats prior unsupervised Chinese word segmentation.

  2. UD-English-CHILDES: A Collected Resource of Gold and Silver Universal Dependencies Trees for Child Language Interactions

    cs.CL 2025-04 conditional novelty 5.0 of 10

    UD-English-CHILDES is the first official Universal Dependencies treebank for child and child-directed speech: 48,183 manually corrected gold sentences (236,941 tokens) plus 1,197,471 silver sentences parsed with stanza.

  3. Context Aware Lemmatization and Morphological Tagging Method in Turkish

    cs.CL 2025-01 reject novelty 3.0 of 10

    A BiLSTM plus Turkish BERT lemmatizer and morphological tagger is tested on IMST and PUD, beating SIGMORPHON 2019 on most metrics but not on PUD lemmatization accuracy, while the paper's claim of being the first conte...

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