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This book is an interdisciplinary study of English binominal quantitative constructions based on English-Chinese comparison. Taking three perspectives, i.e. a functional-typological perspective, a cognitive approach, and a corpus-based method, it aims to unveil the hidden categorisation process behind the usage of English binominal quantitative constructions and to reveal the language universal in cognising the concepts of ‘Quantity’ and ‘Quality’. It argues against treating Chinese and English as members of two opposing typological camps concerning quantification modes (‘classifier languages’ versus ‘non-classifier languages’) and advocates to view the two languages as lying within a more extended and inclusive system, viz. a system of quantification and categorisation modes, or a Quantity-Quality continuum.
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When dealing with a wine, it is of interest to be able to predict its quality based on chemical and/or sensory variables. There is no agreement on what wine quality means, or how it should be assessed and it is often viewed in intrinsic (physicochemical, sensory) or extrinsic (price, prestige, context) terms (Jackson, 2017). In this paper, the wine quality was evaluated by experienced judges who scored the wine on the base of a 0-10 scale, with 0 meaning very bad and 10 excellent, so, the resulting variable was categorical. The models applied to predict this variable provide the prediction of the occurrence probabilities of each of its categories. Nevertheless, jointly with this probabilities' record, the practitioners need the predicted value (category) of the variable, so the statistical problem to be covered refers to the way in which this probabilities' record is transformed into a single value. In this paper we compare the predictive performances of the default method (Bayes Classifier - BC), which assigns a unit to the most likely category, and other two methods (Maximum Difference Classifier and Maximum Ratio Classifier). The BC is the optimal criterion if one is interested in the accuracy of the classification, but, given that it favors the prevalent category most, when there is not a category of interest, it cannot be the best choice. The data under study concern the quality of the red variant of the Portuguese "Vinho Verde" wine (Cortez et al., 2009), measured on a 0-10 scale. Nevertheless, only 6 scores were used, with 2 scores with a very few number of observations, so this is the right context for predictive performance comparisons. In the study, we investigated different merging of categories and we used 11 explanatory variables to estimate the probabilities' record of the wine quality variable.
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When dealing with a wine, it is of interest to be able to predict its quality based on chemical and/or sensory variables. There is no agreement on what wine quality means, or how it should be assessed and it is often viewed in intrinsic (physicochemical, sensory) or extrinsic (price, prestige, context) terms (Jackson, 2017). In this paper, the wine quality was evaluated by experienced judges who scored the wine on the base of a 0-10 scale, with 0 meaning very bad and 10 excellent, so, the resulting variable was categorical. The models applied to predict this variable provide the prediction of the occurrence probabilities of each of its categories. Nevertheless, jointly with this probabilities' record, the practitioners need the predicted value (category) of the variable, so the statistical problem to be covered refers to the way in which this probabilities' record is transformed into a single value. In this paper we compare the predictive performances of the default method (Bayes Classifier - BC), which assigns a unit to the most likely category, and other two methods (Maximum Difference Classifier and Maximum Ratio Classifier). The BC is the optimal criterion if one is interested in the accuracy of the classification, but, given that it favors the prevalent category most, when there is not a category of interest, it cannot be the best choice. The data under study concern the quality of the red variant of the Portuguese "Vinho Verde" wine (Cortez et al., 2009), measured on a 0-10 scale. Nevertheless, only 6 scores were used, with 2 scores with a very few number of observations, so this is the right context for predictive performance comparisons. In the study, we investigated different merging of categories and we used 11 explanatory variables to estimate the probabilities' record of the wine quality variable.
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LANGAGE ET LANGUES --- CLASSIFIERS (LINGUISTICS) --- CLASSIFICATION --- Langues --- Classification
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CATEGORISATION (LINGUISTIQUE) --- CLASSIFIERS (LINGUISTICS) --- GRAMMAR, COMPARATIVE AND GENERAL --- NOMINALS
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"This book investigates nominal arguments in classifier languages. A long-held claim is that classifier languages do not have overt article determiners (D). This book, however, brings to the forefront of the theoretical investigation on the typologically unique Nuosu Yi, a classifier language that will be shown to have an overt article determiner. By comparing nominal arguments in Nuosu Yi to those in Mandarin, the book provides a parametric account of variation among classifier languages and extends the account to argument formation in general. This book begins with a detailed examination of bare numeral classifier phrases in Mandarin by comparing them with bare numeral noun phrases in number marking languages, such as English, French, and Russian. Lastly, the book puts the analysis of Mandarin and Nuosu Yi nominal arguments in a broader, cross-linguistic perspective and develops a parametric account of variation in nominal argument formation in general"--
Classifiers (Linguistics) --- Yi language --- Yi language --- Chinese language --- Chinese language --- Classifiers --- Syntax --- Classifiers --- Syntax
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"Linguists have long been interested in systems of nominal classification due to their diverse functions as well as cognitive and cultural correlates. Among others, ongoing research has focused on semantic, functional and morphosyntactic properties of complex systems such as co-occurring gender and numeral classifiers. Such approaches have typically focused on the languages of north-western South America and Papua New Guinea. This volume proposes to fill in a gap in existing research by focusing on Asia, based on case studies from languages belonging to a wide range of families, i.e., Austroasiatic, Austronesian, Dravidian, Hmong-Mien, Indo-European, Mongolic, Sino-Tibetan and Tai-Kadai as well as the language isolate Nivkh. Gender and classifiers in these languages are approached within several different perspectives, i.e., functional, typological and diachronic, thus revealing complex patterns in their lexical and pragmatic functions as well as origin, development and loss. Describing and analysing such properties is a unique and innovative contribution of the volume"--
Classifiers (Linguistics) --- Grammar, Comparative and general --- Grammar, Comparative and general --- Gender. --- Nominals. --- Asia --- Oceania --- Languages. --- Languages.
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Grammar, Comparative and general --- Definiteness (Linguistics) --- Indefiniteness (Linguistics) --- Determinatives (Linguistics) --- Determiners (Linguistics) --- Determiners. --- Linguistics --- Classifiers (Linguistics) --- Determiners --- Definiteness (Linguistics). --- Philology
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Grammar, Comparative and general --- Pronominals --- Determinatives (Linguistics) --- Determiners (Linguistics) --- Classifiers (Linguistics) --- Definiteness (Linguistics) --- Language and languages --- Syntax --- Determiners --- Pronominal constructions --- Linguistics --- Philology --- Grammar, Comparative and general Syntax
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Grammar, Comparative and general --- Definiteness (Linguistics) --- Indefiniteness (Linguistics) --- Linguistics --- Determinatives (Linguistics) --- Determiners (Linguistics) --- Classifiers (Linguistics) --- Determiners. --- Determiners --- Philology --- Grammar, Comparative and general - Determiners
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