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Simple Search
Hits 1 – 10 of 10
1
The Telegram Chronicles of Online Harm
Scheffler, Tatjana
;
Solopova, Veronika
;
Popa-Wyatt, Mihaela
In: Journal of Open Humanities Data; Vol 7 (2021); 8 ; 2059-481X (2021)
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2
Competition, selection and communicative need in language change: an investigation using corpora, computational modelling and experimentation ...
Karjus, Andres
. - : The University of Edinburgh, 2021
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3
Natural Language Processing for Corpus Linguistics ...
Dunn, Jonathan
. - : Code Ocean, 2021
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4
Reference Corpus of Early New High German (1350–1650) ...
Wegera, Klaus-Peter
;
Solms, Hans-Joachim
;
Demske, Ulrike
. - : Zenodo, 2021
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5
Reference Corpus of Early New High German (1350–1650) ...
Wegera, Klaus-Peter
;
Solms, Hans-Joachim
;
Demske, Ulrike
. - : Zenodo, 2021
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6
Reference Corpus of Early New High German (1350–1650) ...
Wegera, Klaus-Peter
;
Solms, Hans-Joachim
;
Demske, Ulrike
. - : Zenodo, 2021
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7
Reference Corpus of Early New High German (1350–1650) ...
Wegera, Klaus-Peter
;
Solms, Hans-Joachim
;
Demske, Ulrike
. - : Zenodo, 2021
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8
Reference Corpus of Early New High German (1350–1650) ...
Wegera, Klaus-Peter
;
Solms, Hans-Joachim
;
Demske, Ulrike
. - : Zenodo, 2021
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9
Analysis of an Extracted Discipline-Specific Computer Science Vocabulary List
Adya Dutt
;
Dr. Carrie Demmans Epp
. - 2021
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10
Sujeito oculto às claras: uma abordagem descritivo-computacional / Omitted subjects revealed: a quantitative-descriptive approach
Cláudia Freitas
;
Elvis de Souza
In: Revista de Estudos da Linguagem, Vol 29, Iss 2, Pp 1033-1058 (2021) (2021)
Abstract:
Resumo: Neste trabalho, apresentamos estudos descritivos e computacionais relacionados ao sujeito oculto. Em um primeiro momento, realizamos uma descrição de cunho quantitativo, tomando por base três corpora dos gêneros jornalístico, literário e enciclopédico. Especificamente, quantificamos o sujeito oculto em cada um dos corpora, e encontramos sujeitos omitidos em 24%, 41% e 46% das orações, respectivamente. Em um segundo momento, por meio de uma estratégia baseada em regras, reconstituímos esses sujeitos e os devolvemos aos corpora, com o objetivo de avaliar o quanto a omissão do sujeito é capaz de impactar o aprendizado automático de dependências sintáticas. Os resultados indicam que a reconstituição formal do sujeito pode melhorar a aprendizagem das dependências sintáticas em até 2% quando consideramos a métrica CLAS, evidenciando o papel relevante da modelagem linguística no aprendizado automático. Palavras-chave: descrição linguística; sujeito oculto; omissão de sujeito; dependências sintáticas; linguística computacional; aprendizado de máquina; linguística de corpus. Abstract: In this paper, we present descriptive and computational studies related to omitted subjects. Firstly, we develop a quantitative descriptive study based on three corpora, which consist of journalistic, literary and encyclopedic genres. Specifically, we quantify the omitted subjects in sentences for each of these corpora; omitted subjects were found in 24%, 41% and 46% of their sentences, respectively. Secondly, applying rule-based strategies, we reconstitute those subjects and place them back to the corpora, with the goal of evaluating how much the omission of subjects can impact the automatic learning of syntactic dependencies. The results indicate that the formal subject reconstitution can enhance the learning of syntactic dependencies in up to 2% according to the CLAS metric, highlighting the relevant role of linguistic modeling in the automatic learning process. Keywords: linguistic description; omitted subject; syntactic dependencies; computational linguistics; machine learning; corpus linguistics.
Keyword:
aprendizado de máquina
;
computational linguistics
;
dependências sintáticas
;
descrição linguística
;
Language and Literature
;
linguistic description
;
linguística computacional
;
linguística de corpus
;
machi
;
omissão de sujeito
;
omitted subject
;
P
;
P1-1091
;
Philology. Linguistics
;
sujeito oculto
;
syntactic dependencies
URL:
https://doaj.org/article/58f7a3d0fef84262ba5dff16aea6e223
https://doi.org/10.17851/2237-2083.29.2.1033-1058
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