Joakim Nivre
6 papers in the PaperMetrix corpus
Papers by this author
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Modeling the Statistical Idiosyncrasy of Multiword Expressions
2015
The focus of this work is statistical idiosyncrasy (or collocational weight) as a discriminant property of multiword expressions. We formalize and model this property, compile a 2-class data set of MWE and non-MWE examples, and …
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Universal Dependencies v1: A Multilingual Treebank Collection
2016
Cross-linguistically consistent annotation is necessary for sound comparative evaluation and cross-lingual learning experiments.It is also useful for multilingual system development and comparative linguistic studies.Universal Dependencies is an open community effort to create cross-linguistically consistent treebank …
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Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection
2020 · Uppsala University Publications (Uppsala University)
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, …
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CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
2017
Daniel Zeman, Martin Popel, Milan Straka, Jan Hajič, Joakim Nivre, Filip Ginter, Juhani Luotolahti, Sampo Pyysalo, Slav Petrov, Martin Potthast, Francis Tyers, Elena Badmaeva, Memduh Gokirmak, Anna Nedoluzhko, Silvie Cinková, Jan Hajič jr., Jaroslava Hlaváčová, …
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Universal Dependencies
2021 · Computational Linguistics
Abstract Universal dependencies (UD) is a framework for morphosyntactic annotation of human language, which to date has been used to create treebanks for more than 100 languages. In this article, we outline the linguistic theory …
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CoNLL 2018 Shared Task : Multilingual Parsing from Raw Text to Universal Dependencies
2018 · Conference on Computational Natural Language Learning
Every year, the Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2018, one of two tasks was …