Identifying Passive Clause Patterns Based on Self-Organizing Maps

Detalhes bibliográficos
Autor(a) principal: Laurett Neves Damasceno, Gesieny
Data de Publicação: 2023
Outros Autores: Virginia Rodrigues, Violeta
Tipo de documento: Artigo
Idioma: por
Título da fonte: Revista odisséia
Texto Completo: https://periodicos.ufrn.br/odisseia/article/view/32258
Resumo: The aim of the current article is to lay the foundations for thedevelopment of a descriptive model capable of differentiating several configurationsembodied by passive clauses formed with auxiliary verb ser (to be) + past participleof the main verb in specific interaction contexts, based on using the Neural Networkof Self-Organizing Maps (SOM Network). All 220 constitutive clauses forming theherein analyzed corpus were allocated by the SOM Network in 46 neurons, whichrepresent different samples of passive clauses, by concomitantly taking intoconsideration the classes of all eight parameters selected in the current research.These 46 samples reflect variations in the grammatical and pragmatic-discursiveconfigurations of passive clauses, in a quite thorough manner, by taking intoconsideration the theoretical apparatus of Systemic-Functional Grammar. Thedefinition of the optimal number of groups allowed us to deduce data from threeimportant groupings that have portrayed the major meanings elicited by the analyzedpassive clauses within the scope of all 102 analyzed journalistic news.
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spelling Identifying Passive Clause Patterns Based on Self-Organizing MapsReconhecimento de padrões de orações passivas por meio de Mapas Auto-organizáveisPassive clausesSystemic-Functional GrammarNeural Network of Self-Organizing MapsOrações passivasGramática Sistêmico-FuncionalRede Neural de Mapas Auto-organizáveisThe aim of the current article is to lay the foundations for thedevelopment of a descriptive model capable of differentiating several configurationsembodied by passive clauses formed with auxiliary verb ser (to be) + past participleof the main verb in specific interaction contexts, based on using the Neural Networkof Self-Organizing Maps (SOM Network). All 220 constitutive clauses forming theherein analyzed corpus were allocated by the SOM Network in 46 neurons, whichrepresent different samples of passive clauses, by concomitantly taking intoconsideration the classes of all eight parameters selected in the current research.These 46 samples reflect variations in the grammatical and pragmatic-discursiveconfigurations of passive clauses, in a quite thorough manner, by taking intoconsideration the theoretical apparatus of Systemic-Functional Grammar. Thedefinition of the optimal number of groups allowed us to deduce data from threeimportant groupings that have portrayed the major meanings elicited by the analyzedpassive clauses within the scope of all 102 analyzed journalistic news.O presente artigo busca lançar as bases para o desenvolvimento de ummodelo descritivo capaz de distinguir, por intermédio da Rede Neural de MapasAuto-organizáveis (Rede SOM), as diversas configurações que as orações passivasformadas com verbo auxiliar ser + particípio passado do verbo principal assumemem contextos específicos de interação. As 220 orações constitutivas do corpusforam alocadas, pela Rede SOM, em 46 neurônios, que representam diferentesexemplares de orações passivas, considerando, concomitantemente, as classes dosoito parâmetros selecionados por esta pesquisa. Esses 46 exemplares refletem, deforma bastante criteriosa, as variações das orações passivas em termos deconfigurações gramatical e pragmático-discursiva, considerando o aparato teórico daGramática Sistêmico-Funcional. A definição do número ótimo de grupos possibilitou,ainda, a depreensão dos dados a partir de, basicamente, três importantesagrupamentos, que retratam os principais significados evocados pelas oraçõespassivas no âmbito das 102 notícias jornalísticas analisadas.UFRN2023-06-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.ufrn.br/odisseia/article/view/3225810.21680/1983-2435.2023v8n1ID32258Odisseia; Vol. 8 No. 1 (2023): Revista Odisseia; 36-56Revue Odisseia; Vol. 8 No. 1 (2023): Revista Odisseia; 36-56Revista Odisseia; v. 8 n. 1 (2023): Revista Odisseia; 36-561983-243510.21680/1983-2435.2023v8n1reponame:Revista odisséiainstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNporhttps://periodicos.ufrn.br/odisseia/article/view/32258/17032Copyright (c) 2023 Revista Odisseiahttp://creativecommons.org/licenses/by-nc-sa/4.0info:eu-repo/semantics/openAccessLaurett Neves Damasceno, GesienyVirginia Rodrigues, Violeta2023-06-23T16:59:32Zoai:periodicos.ufrn.br:article/32258Revistahttps://periodicos.ufrn.br/odisseia/indexPUBhttps://periodicos.ufrn.br/odisseia/oai||revistaodisseia2016@gmail.com1983-24351983-2435opendoar:2023-06-23T16:59:32Revista odisséia - Universidade Federal do Rio Grande do Norte (UFRN)false
dc.title.none.fl_str_mv Identifying Passive Clause Patterns Based on Self-Organizing Maps
Reconhecimento de padrões de orações passivas por meio de Mapas Auto-organizáveis
title Identifying Passive Clause Patterns Based on Self-Organizing Maps
spellingShingle Identifying Passive Clause Patterns Based on Self-Organizing Maps
Laurett Neves Damasceno, Gesieny
Passive clauses
Systemic-Functional Grammar
Neural Network of Self-Organizing Maps
Orações passivas
Gramática Sistêmico-Funcional
Rede Neural de Mapas Auto-organizáveis
title_short Identifying Passive Clause Patterns Based on Self-Organizing Maps
title_full Identifying Passive Clause Patterns Based on Self-Organizing Maps
title_fullStr Identifying Passive Clause Patterns Based on Self-Organizing Maps
title_full_unstemmed Identifying Passive Clause Patterns Based on Self-Organizing Maps
title_sort Identifying Passive Clause Patterns Based on Self-Organizing Maps
author Laurett Neves Damasceno, Gesieny
author_facet Laurett Neves Damasceno, Gesieny
Virginia Rodrigues, Violeta
author_role author
author2 Virginia Rodrigues, Violeta
author2_role author
dc.contributor.author.fl_str_mv Laurett Neves Damasceno, Gesieny
Virginia Rodrigues, Violeta
dc.subject.por.fl_str_mv Passive clauses
Systemic-Functional Grammar
Neural Network of Self-Organizing Maps
Orações passivas
Gramática Sistêmico-Funcional
Rede Neural de Mapas Auto-organizáveis
topic Passive clauses
Systemic-Functional Grammar
Neural Network of Self-Organizing Maps
Orações passivas
Gramática Sistêmico-Funcional
Rede Neural de Mapas Auto-organizáveis
description The aim of the current article is to lay the foundations for thedevelopment of a descriptive model capable of differentiating several configurationsembodied by passive clauses formed with auxiliary verb ser (to be) + past participleof the main verb in specific interaction contexts, based on using the Neural Networkof Self-Organizing Maps (SOM Network). All 220 constitutive clauses forming theherein analyzed corpus were allocated by the SOM Network in 46 neurons, whichrepresent different samples of passive clauses, by concomitantly taking intoconsideration the classes of all eight parameters selected in the current research.These 46 samples reflect variations in the grammatical and pragmatic-discursiveconfigurations of passive clauses, in a quite thorough manner, by taking intoconsideration the theoretical apparatus of Systemic-Functional Grammar. Thedefinition of the optimal number of groups allowed us to deduce data from threeimportant groupings that have portrayed the major meanings elicited by the analyzedpassive clauses within the scope of all 102 analyzed journalistic news.
publishDate 2023
dc.date.none.fl_str_mv 2023-06-15
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://periodicos.ufrn.br/odisseia/article/view/32258
10.21680/1983-2435.2023v8n1ID32258
url https://periodicos.ufrn.br/odisseia/article/view/32258
identifier_str_mv 10.21680/1983-2435.2023v8n1ID32258
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://periodicos.ufrn.br/odisseia/article/view/32258/17032
dc.rights.driver.fl_str_mv Copyright (c) 2023 Revista Odisseia
http://creativecommons.org/licenses/by-nc-sa/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2023 Revista Odisseia
http://creativecommons.org/licenses/by-nc-sa/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv UFRN
publisher.none.fl_str_mv UFRN
dc.source.none.fl_str_mv Odisseia; Vol. 8 No. 1 (2023): Revista Odisseia; 36-56
Revue Odisseia; Vol. 8 No. 1 (2023): Revista Odisseia; 36-56
Revista Odisseia; v. 8 n. 1 (2023): Revista Odisseia; 36-56
1983-2435
10.21680/1983-2435.2023v8n1
reponame:Revista odisséia
instname:Universidade Federal do Rio Grande do Norte (UFRN)
instacron:UFRN
instname_str Universidade Federal do Rio Grande do Norte (UFRN)
instacron_str UFRN
institution UFRN
reponame_str Revista odisséia
collection Revista odisséia
repository.name.fl_str_mv Revista odisséia - Universidade Federal do Rio Grande do Norte (UFRN)
repository.mail.fl_str_mv ||revistaodisseia2016@gmail.com
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