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Durán Migdalia - One of the best experts on this subject based on the ideXlab platform.
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Sintaxis y pragmática: dos niveles de análisis en el tema del enunciado en el habla del español de Venezuela
'Todas as Letras: Revista de Lingua e Literatura', 2021Co-Authors: Durán MigdaliaAbstract:The aim of this work is to analyze the issue of the utterance in the Spanish of Venezuela, in order to explain how this issue is integrated or related to the syntactic functions. Theoretically, the study is supported on functional syntax and on the syntax of spoken language. Regarding the methodology, it follows the functional discursive approach (Halliday 1967, 1985). The corpus studied was the Corpus del Laboratorio de Fonética de la Universidad de Los Andes, from which eight informants were selected. The sample gathered 450 utterances with conversation themes encoded in the Nominal group. As a result, we find that the topic as thematic structure is expressed through syntactic structures such as: subject (53,7%), direct object (21,6%), indirect object (14,3%) and Nominal Attribute (10,4%). The sentence utterances show pre-position or post-position of the theme with a function of direct or indirect object. The functions fulfilled by this thematized group are those of topicalization (Top) and/or right displacement (D.D in Spanish) or left displacement (D.I in Spanish). As a conclusion, from the perspective of functional syntax, the theme is not exclusively the subject, it can be classified under different syntactic functions and it can be at the beginning or in final position of the utterance. El objetivo de este trabajo es analizar el tema del enunciado en el habla del español de Venezuela, para explicar cómo se integra o se relaciona el tema con las funciones sintácticas. Teóricamente, este estudio se fundamenta en la sintaxis funcional y en la sintaxis de la lengua oral, y con respecto a la metodología, sigue el enfoque discursivo funcional (Halliday 1967, 1985). El corpus de estudio estuvo constituido por el Corpus del Laboratorio de Fonética de la Universidad de Los Andes. De éste se seleccionaron ocho informantes. La muestra estuvo conformada por 450 enunciados con temas de conversación codificados en el sintagma Nominal. Como resultado se tiene que el tema como estructura temática se expresa a través de funciones sintácticas tales como: sujeto (53,7%), objeto directo (21,6%), objeto indirecto (14,3%) y atributo Nominal (10,4%). Los enunciados oracionales presentan anteposición o posposición del tema con función de objeto directo o indirecto. Las funciones que cumple este sintagma tematizado son topicalización (Top) y/o dislocación a la derecha (D.D) o a la izquierda (D.I). En conclusión, desde la perspectiva de la sintaxis funcional, el tema no es exclusivo del sujeto, puede codificarse bajo diferentes funciones sintácticas, y puede estar al inicio o al final del enunciado
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Sintaxis y pragmática: dos niveles de análisis en el tema del enunciado en el habla del español de Venezuela
'Todas as Letras: Revista de Lingua e Literatura', 2021Co-Authors: Durán MigdaliaAbstract:ResumenEl objetivo de este trabajo es analizar el tema del enunciado en el habla del español de Venezuela, para explicar cómo se integra o se relaciona el tema con las funciones sintácticas. Teóricamente, este estudio se fundamenta en la sintaxis funcional y en la sintaxis de la lengua oral, y con respecto a la metodología, sigue el enfoque discursivo funcional (Halliday 1967, 1985). El corpus de estudio estuvo constituido por el Corpus del Laboratorio de Fonética de la Universidad de Los Andes. De éste se seleccionaron ocho informantes. La muestra estuvo conformada por 450 enunciados con temas de conversación codificados en el sintagma Nominal. Como resultado se tiene que el tema como estructura temática se expresa a través de funciones sintácticas tales como: sujeto (53,7%), objeto directo (21,6%), objeto indirecto (14,3%) y atributo Nominal (10,4%). Los enunciados oracionales presentan anteposición o posposición del tema con función de objeto directo o indirecto. Las funciones que cumple este sintagma tematizado son topicalización (Top) y/o dislocación a la derecha (D.D) o a la izquierda (D.I). En conclusión, desde la perspectiva de la sintaxis funcional, el tema no es exclusivo del sujeto, puede codificarse bajo diferentes funciones sintácticas, y puede estar al inicio o al final del enunciado. Palabras claves: Tema del enunciado, estructura sintáctica, español de Venezuela. Recepción: 25/09/2014 Evaluación: 27/01/2015 Recepción de la versión definitiva: 28/01/2015 SYNTAX AND PRAGMATICS: TWO LEVELS OF ANALYSIS IN THE ISSUE OF THE UTTERANCE IN THE SPANISH OF VENEZUELA AbstractThe aim of this work is to analyze the issue of the utterance in the Spanish of Venezuela, in order to explain how this issue is integrated or related to the syntactic functions. Theoretically, the study is supported on functional syntax and on the syntax of spoken language. Regarding the methodology, it follows the functional discursive approach (Halliday 1967, 1985). The corpus studied was the Corpus del Laboratorio de Fonética de la Universidad de Los Andes, from which eight informants were selected. The sample gathered 450 utterances with conversation themes encoded in the Nominal group. As a result, we find that the topic as thematic structure is expressed through syntactic structures such as: subject (53,7%), direct object (21,6%), indirect object (14,3%) and Nominal Attribute (10,4%). The sentence utterances show pre-position or post-position of the theme with a function of direct or indirect object. The functions fulfilled by this thematized group are those of topicalization (Top) and/or right displacement (D.D in Spanish) or left displacement (D.I in Spanish). As a conclusion, from the perspective of functional syntax, the theme is not exclusively the subject, it can be classified under different syntactic functions and it can be at the beginning or in final position of the utterance. Key words: utterance theme, syntactic structure, Spanish of Venezuela SYNTAXE ET PRAGMATIQUE: DEUX NIVEAUX D’ANALYSES DANS LE THÈME DE L’ÉNONCÉ DANS LE PARLER DE L’ESPAGNOL DU VENEZUELA RésuméLe but de ce travail est d’analyser le theme de l’énoncé dans le parler de l’espagnol du Venezuela afin d’expliquer comment le theme et les fonctions syntaxiques s’intégrent ou se rattachent. Cette etude s’appuie théoriquement sur la syntaxe fonctionnelle et la syntaxe de la langue orale. Du point méthodologique, elle suit l’approche discursive fonctionnelle (Halliday 1967, 1985). Le corpus de l’étude a été constitué par le Corpus du Laboratoire de Phonétique de l’Universidad de Los Andes d’où on a sélectionné huit informateurs. L’échantillon a compris 450 énoncés portant sur des thèmes de conversation codifiés dans le syntagme Nominal. Les résultats nous montrent que le thème comme structure thématique est exprimé par le biais de fonctions syntaxiques telles que : sujet (53,7%), objet direct (21,6%), objet indirect (14,3%) et attribut Nominal (10,4%). Les énoncés propositionnels présentent antéposition ou postposition du thème avec fonction d’objet direct ou indirect. Les fonctions accomplies par ce syntagme thématisé sont la topicalisation (Top) et / ou dislocation à droite (DD) ou à gauche (DG). En conclusion, d’après la perspective de la syntaxe fonctionnelle, le thème n’est pas exclusif du sujet. Il peut se codifier selon d’autres fonctions syntaxiques et peut être placé au début ou à la fin de l’énoncé. Mots clé: thème de l’énoncé, structure syntaxique, espagnol du Venezuela. SINTASSI E PRAGMATICA: DUE LIVELLI DELL’ANALISI NELL’ARGOMENTO DELL’ENUNCIATO NELLALINGUA PARLATA DELLO SPAGNOLO DEL VENEZUELA RiassuntoQuest’articolo ha lo scopo di analizzare l’argomento dell’enunciato nella lingua parlata dello spagnolo del Venezuela, per spiegare come si integra o si collega quest’argomento con le funzioni sintattiche. Dal punto di vista teorico, questo studio si basa nella sintassi funzionale e nella sintassi della lingua parlata; Dal punto di vista metodologico, usa l’approccio discorsivo funzionale (Halliday 1967, 1985). Il corpus dello studio fu costituito dal Corpus del Laboratorio di Fonetica dell’Università delle Ande. Sono stati scelti otto informanti. La mostra è stata conformata da 450 enunciati con argomenti di conversazione codificati nel sintagma Nominale. Gli esiti dimostrano che l’argomento come struttura si esprime attraverso funzioni sintattiche come: soggetto (53,7 %), complemento oggetto (14,3 %), complemento Nominale ((14,3 %) e predicato Nominale (10,4%). Gli enunciati delle frasi presentano anteposizione o posposizione dell’argomento con funzione di complemento oggetto o complemento Nominale. Le funzioni che compie il sintagma sono topicalizzazione (Top) e/o dislocazione a destra (D.D) o a sinistra (D.S). In conclusione, dalla prospettiva della sintassi funzionale, l’argomento non è soltanto del soggetto ; può essere codificato sotto funzioni sintattiche diverse e può essere all’inizio o alla fine dell’enunciato. Parole chiavi: Argomento dell’enunciato. Struttura sintattica. Spagnolo del Venezuela.
Yiu-ming Cheung - One of the best experts on this subject based on the ideXlab platform.
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a new distance metric exploiting heterogeneous interAttribute relationship for ordinal and Nominal Attribute data clustering
IEEE Transactions on Systems Man and Cybernetics, 2020Co-Authors: Yiqun Zhang, Yiu-ming CheungAbstract:Ordinal Attribute has all the common characteristics of a Nominal one but it differs from the Nominal one by having naturally ordered possible values (also called categories interchangeably). In clustering analysis tasks, categorical data composed of both ordinal and Nominal Attributes (also called mixed-categorical data interchangeably) are common. Under this circumstance, existing distance and similarity measures suffer from at least one of the following two drawbacks: 1) directly treat ordinal Attributes as Nominal ones, and thus ignore the order information from them and 2) suppose all the Attributes are independent of each other, measure the distance between two categories from a target Attribute without considering the valuable information provided by the other Attributes that correlate with the target one. These two drawbacks may twist the natural distances of Attributes and further lead to unsatisfactory clustering results. This article, therefore, presents an entropy-based distance metric that quantifies the distance between categories by exploiting the information provided by different Attributes that correlate with the target one. It also preserves the order relationship among ordinal categories during the distance measurement. Since Attributes are usually correlated in different degrees, we also define the interdependence between different types of Attributes to weight their contributions in forming distances. The proposed metric overcomes the two above-mentioned drawbacks for mixed-categorical data clustering. More important, it conceptually unifies the distances of ordinal and Nominal Attributes to avoid information loss during clustering. Moreover, it is parameter free, and will not bring extra computational cost compared to the existing state-of-the-art counterparts. Extensive experiments show the superiority of the proposed distance metric.
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A Unified Entropy-Based Distance Metric for Ordinal-and-Nominal-Attribute Data Clustering
IEEE Transactions on Neural Networks and Learning Systems, 2020Co-Authors: Yiqun Zhang, Yiu-ming CheungAbstract:Ordinal data are common in many data mining and machine learning tasks. Compared to Nominal data, the possible values (also called categories interchangeably) of an ordinal Attribute are naturally ordered. Nevertheless, since the data values are not quantitative, the distance between two categories of an ordinal Attribute is generally not well defined, which surely has a serious impact on the result of the quantitative analysis if an inappropriate distance metric is utilized. From the practical perspective, ordinal-and-Nominal-Attribute categorical data, i.e., categorical data associated with a mixture of Nominal and ordinal Attributes, is common, but the distance metric for such data has yet to be well explored in the literature. In this paper, within the framework of clustering analysis, we therefore first propose an entropy-based distance metric for ordinal Attributes, which exploits the underlying order information among categories of an ordinal Attribute for the distance measurement. Then, we generalize this distance metric and propose a unified one accordingly, which is applicable to ordinal-and-Nominal-Attribute categorical data. Compared with the existing metrics proposed for categorical data, the proposed metric is simple to use and nonparametric. More importantly, it reasonably exploits the underlying order information of ordinal Attributes and statistical information of Nominal Attributes for distance measurement. Extensive experiments show that the proposed metric outperforms the existing counterparts on both the real and benchmark data sets.
Yiqun Zhang - One of the best experts on this subject based on the ideXlab platform.
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a new distance metric exploiting heterogeneous interAttribute relationship for ordinal and Nominal Attribute data clustering
IEEE Transactions on Systems Man and Cybernetics, 2020Co-Authors: Yiqun Zhang, Yiu-ming CheungAbstract:Ordinal Attribute has all the common characteristics of a Nominal one but it differs from the Nominal one by having naturally ordered possible values (also called categories interchangeably). In clustering analysis tasks, categorical data composed of both ordinal and Nominal Attributes (also called mixed-categorical data interchangeably) are common. Under this circumstance, existing distance and similarity measures suffer from at least one of the following two drawbacks: 1) directly treat ordinal Attributes as Nominal ones, and thus ignore the order information from them and 2) suppose all the Attributes are independent of each other, measure the distance between two categories from a target Attribute without considering the valuable information provided by the other Attributes that correlate with the target one. These two drawbacks may twist the natural distances of Attributes and further lead to unsatisfactory clustering results. This article, therefore, presents an entropy-based distance metric that quantifies the distance between categories by exploiting the information provided by different Attributes that correlate with the target one. It also preserves the order relationship among ordinal categories during the distance measurement. Since Attributes are usually correlated in different degrees, we also define the interdependence between different types of Attributes to weight their contributions in forming distances. The proposed metric overcomes the two above-mentioned drawbacks for mixed-categorical data clustering. More important, it conceptually unifies the distances of ordinal and Nominal Attributes to avoid information loss during clustering. Moreover, it is parameter free, and will not bring extra computational cost compared to the existing state-of-the-art counterparts. Extensive experiments show the superiority of the proposed distance metric.
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A Unified Entropy-Based Distance Metric for Ordinal-and-Nominal-Attribute Data Clustering
IEEE Transactions on Neural Networks and Learning Systems, 2020Co-Authors: Yiqun Zhang, Yiu-ming CheungAbstract:Ordinal data are common in many data mining and machine learning tasks. Compared to Nominal data, the possible values (also called categories interchangeably) of an ordinal Attribute are naturally ordered. Nevertheless, since the data values are not quantitative, the distance between two categories of an ordinal Attribute is generally not well defined, which surely has a serious impact on the result of the quantitative analysis if an inappropriate distance metric is utilized. From the practical perspective, ordinal-and-Nominal-Attribute categorical data, i.e., categorical data associated with a mixture of Nominal and ordinal Attributes, is common, but the distance metric for such data has yet to be well explored in the literature. In this paper, within the framework of clustering analysis, we therefore first propose an entropy-based distance metric for ordinal Attributes, which exploits the underlying order information among categories of an ordinal Attribute for the distance measurement. Then, we generalize this distance metric and propose a unified one accordingly, which is applicable to ordinal-and-Nominal-Attribute categorical data. Compared with the existing metrics proposed for categorical data, the proposed metric is simple to use and nonparametric. More importantly, it reasonably exploits the underlying order information of ordinal Attributes and statistical information of Nominal Attributes for distance measurement. Extensive experiments show that the proposed metric outperforms the existing counterparts on both the real and benchmark data sets.
Kwang Ryel Ryu - One of the best experts on this subject based on the ideXlab platform.
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sampling of virtual examples to improve classification accuracy for Nominal Attribute data
Lecture Notes in Computer Science, 2006Co-Authors: Yujung Lee, Jaeho Kang, Byoungho Kang, Kwang Ryel RyuAbstract:This paper presents a method of using virtual examples to improve the classification accuracy for data with Nominal Attributes. Most of the previous researches on virtual examples focused on data with numeric Attributes, and they used domain-specific knowledge to generate useful virtual examples for a particularly targeted learning algorithm. Instead of using domain-specific knowledge, our method samples virtual examples from a naive Bayesian network constructed from the given training set. A sampled example is considered useful if it contributes to the increment of the network's conditional likelihood when added to the training set. A set of useful virtual examples can be collected by repeating this process of sampling followed by evaluation. Experiments have shown that the virtual examples collected this way can help various learning algorithms to derive classifiers of improved accuracy.
Yujung Lee - One of the best experts on this subject based on the ideXlab platform.
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sampling of virtual examples to improve classification accuracy for Nominal Attribute data
Lecture Notes in Computer Science, 2006Co-Authors: Yujung Lee, Jaeho Kang, Byoungho Kang, Kwang Ryel RyuAbstract:This paper presents a method of using virtual examples to improve the classification accuracy for data with Nominal Attributes. Most of the previous researches on virtual examples focused on data with numeric Attributes, and they used domain-specific knowledge to generate useful virtual examples for a particularly targeted learning algorithm. Instead of using domain-specific knowledge, our method samples virtual examples from a naive Bayesian network constructed from the given training set. A sampled example is considered useful if it contributes to the increment of the network's conditional likelihood when added to the training set. A set of useful virtual examples can be collected by repeating this process of sampling followed by evaluation. Experiments have shown that the virtual examples collected this way can help various learning algorithms to derive classifiers of improved accuracy.