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87 - Extracting valences from a dependency treebank for populating the verb lexicon of a Portuguese HPSG grammar ...
PROPOR 2022 2022; Alencar, Leonel F. de; Coutinho, Lucas R.. - : Underline Science Inc., 2022
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Modeling verb valency in a computational grammar for Portuguese in the HPSG formalism ; Modelação da valência verbal numa gramática computacional do português no formalismo HPSG
In: Domínios de Lingu@gem; Ahead of Print; 1-63 ; 1980-5799 (2022)
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Universal Dependencies 2.9
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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Universal Dependencies 2.8.1
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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Universal Dependencies 2.8
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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Explorando a revisão de corpora por meio da comparação de regras gramaticais em padrões sintáticos ...
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The GlobalWordNet Formats: Updates for 2020 ...
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The GlobalWordNet Formats: Updates for 2020 ...
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On the Utility of Word Embeddings for Enriching OpenWordNet-PT ...
Gonçalo Oliveira, Hugo; Aguiar, Fredson Silva de Souza; Rademaker, Alexandre. - : Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021
Abstract: The maintenance of wordnets and lexical knwoledge bases typically relies on time-consuming manual effort. In order to minimise this issue, we propose the exploitation of models of distributional semantics, namely word embeddings learned from corpora, in the automatic identification of relation instances missing in a wordnet. Analogy-solving methods are first used for learning a set of relations from analogy tests focused on each relation. Despite their low accuracy, we noted that a portion of the top-given answers are good suggestions of relation instances that could be included in the wordnet. This procedure is applied to the enrichment of OpenWordNet-PT, a public Portuguese wordnet. Relations are learned from data acquired from this resource, and illustrative examples are provided. Results are promising for accelerating the identification of missing relation instances, as we estimate that about 17% of the potential suggestions are good, a proportion that almost doubles if some are automatically invalidated. ... : OASIcs, Vol. 93, 3rd Conference on Language, Data and Knowledge (LDK 2021), pages 21:1-21:13 ...
Keyword: analogy tests; Computing methodologies → Language resources; Computing methodologies → Lexical semantics; lexical resources; word embeddings; wordnet
URL: https://dx.doi.org/10.4230/oasics.ldk.2021.21
https://drops.dagstuhl.de/opus/volltexte/2021/14557/
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Utilizando um Dicionário Morfológico para expandir a cobertura lexical de uma Gramática do Português no formalismo HPSG ...
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Morpho-syntactically annotated corpora provided for the PARSEME Shared Task on Semi-Supervised Identification of Verbal Multiword Expressions (edition 1.2)
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Universal Dependencies 2.7
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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Annotated corpora and tools of the PARSEME Shared Task on Semi-Supervised Identification of Verbal Multiword Expressions (edition 1.2)
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Universal Dependencies 2.6
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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Universal Dependencies 2.5
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019
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Universal Dependencies 2.4
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019
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Linked Open Treebanks. Interlinking Syntactically Annotated Corpora in the LiLa Knowledge Base of Linguistic Resources for Latin
Mambrini, Francesco (orcid:0000-0003-0834-7562); Passarotti, Marco (orcid:0000-0002-9806-7187). - : Association for Computational Linguistics, 2019. : country:FRA, 2019. : place:Paris, 2019
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Universal Dependencies 2.2
In: https://hal.archives-ouvertes.fr/hal-01930733 ; 2018 (2018)
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Universal Dependencies 2.3
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018
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Universal Dependencies 2.2
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018
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