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1
Universal Dependencies 2.9
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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2
Universal Dependencies 2.8.1
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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3
Universal Dependencies 2.8
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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4
Universal Dependencies 2.7
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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5
Universal Dependencies 2.6
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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6
Universal Dependencies 2.5
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019
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7
Universal Dependencies 2.4
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019
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8
Universal Dependencies 2.2
In: https://hal.archives-ouvertes.fr/hal-01930733 ; 2018 (2018)
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9
Universal Dependencies 2.3
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018
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10
Universal Dependencies 2.2
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018
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11
Universal Dependencies 2.1
In: https://hal.inria.fr/hal-01682188 ; 2017 (2017)
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12
Universal Dependencies 2.0 – CoNLL 2017 Shared Task Development and Test Data
Nivre, Joakim; Agić, Željko; Ahrenberg, Lars. - : Universal Dependencies Consortium, 2017
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13
Universal Dependencies 2.1
Nivre, Joakim; Agić, Željko; Ahrenberg, Lars. - : Universal Dependencies Consortium, 2017
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14
Handling unknown words in statistical latent-variable parsing models for Arabic, English and French
In: Attia, Mohammed, Foster, Jennifer orcid:0000-0002-7789-4853 , Hogan, Deirdre, Le Roux, Joseph, Tounsi, Lamia and van Genabith, Josef orcid:0000-0003-1322-7944 (2010) Handling unknown words in statistical latent-variable parsing models for Arabic, English and French. In: SPMRL 2010 - 1st Workshop on Statistical Parsing of Morphologically-Rich Languages at NAACL HLT 2010, 5 June 2010, Los Angeles, CA, USA. (2010)
Abstract: This paper presents a study of the impact of using simple and complex morphological clues to improve the classification of rare and unknown words for parsing. We compare this approach to a language-independent technique often used in parsers which is based solely on word frequencies. This study is applied to three languages that exhibit different levels of morphological expressiveness: Arabic, French and English. We integrate information about Arabic affixes and morphotactics into a PCFG-LA parser and obtain stateof-the-art accuracy. We also show that these morphological clues can be learnt automatically from an annotated corpus.
Keyword: Machine translating
URL: http://doras.dcu.ie/15980/
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15
Handling Unknown Words in Statistical Latent-Variable Parsing Models for Arabic, English and French
In: Proceedings of the First Workshop on Statistical Parsing of Morphologically Rich Languages (SPMRL 2010) ; First Workshop on Statistical Parsing of Morphologically Rich Languages (SPMRL 2010) ; https://hal.archives-ouvertes.fr/hal-00702414 ; First Workshop on Statistical Parsing of Morphologically Rich Languages (SPMRL 2010), 2010, United States. pp.67-75 (2010)
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