Classificação dos verbos manuais na libras: uma nova abordagem de configuração de mão
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Classificação dos verbos manuais na libras: uma nova abordagem de configuração de mão

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Introduction

Classificação dos verbos manuais na libras: uma nova abordagem de configuração de mão. Explore uma nova abordagem para classificar verbos manuais na Libras, baseada em classificadores semânticos e blends, superando categorias anteriores de configuração de mão.

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Abstract

Os verbos manuais na libras podem ser divididos em locativos, classificadores de entidade ou classificadores de instrumento (FARIA-NASCIMENTO; CORREIA, 2011). Pelo viés da Morfologia Distribuída (HALLE; MARANTZ, 1993), Mak (2021) selecionou 8 verbos manuais, totalizando 37 entradas lexicais retiradas do Dicionário da Língua de Sinais do Brasil (CAPOVILLA et al., 2017), e verificou que parte deles não se enquadra nas categorias propostas por Faria-Nascimento e Correia (2011). Dessa forma, baseando-se nos tipos de formação de verbos: derivação, composição e fusão (blend) (XAVIER; NEVES, 2011), a autora propôs a classe de verbos com configuração de mão classificadora, onde todos os verbos são formados por classificadores SASSes (SUPALLA, 1986) e por blends. Este artigo analisa a proposta de Mak (2021) e sugere que os verbos da nova classe são formados por classificadores semânticos (SUPALLA, 1986) e podem ser formados por blend e por composição.


Review

This paper addresses a significant and timely topic within the field of Libras (Brazilian Sign Language) linguistics: the classification of manual verbs. Building upon established classifications (Faria-Nascimento; Correia, 2011) that categorize manual verbs as locative, entity, or instrument classifiers, the article highlights a crucial gap. It references Mak (2021)'s work, which, utilizing a Distributed Morphology framework, identified a subset of manual verbs from the Libras dictionary that do not neatly fit these existing categories. Mak's innovative response was to propose a new class of "verbs with classifying handshape," formed by SASSes and blends. This article steps in to critically analyze Mak's proposal, aiming to refine it by suggesting that these verbs are more accurately characterized as being formed by *semantic classifiers* (Supalla, 1986) and can involve both blend and composition as formation processes. The strength of this work lies in its critical engagement with previous scholarship and its commitment to achieving a more precise and comprehensive linguistic description of Libras. By identifying limitations in current verb classifications and actively proposing refinements, the authors contribute to a more nuanced understanding of morphology and lexical structure in sign languages. The re-evaluation of Mak's (2021) "classifying handshape" in favor of "semantic classifiers" (Supalla, 1986) suggests a deeper theoretical grounding and potentially greater explanatory power. Furthermore, including "composition" alongside "blend" as a verb formation mechanism enriches the morphological analysis, moving towards a more complete account of how manual verbs are constructed in Libras. This iterative process of refinement is essential for the advancement of sign language linguistics. While the abstract clearly outlines a valuable contribution, a more detailed presentation of the methodology would enhance its impact. For instance, explaining *how* the analysis of Mak's (2021) proposal was conducted, including the specific criteria used to differentiate between "classifying handshape" and "semantic classifiers" or to identify instances of composition, would strengthen the claims. Future work could also explore the empirical implications of this refined classification through a larger corpus study or cross-linguistic comparisons, examining if these "semantic classifier" verbs behave distinctively in syntactic contexts. Nonetheless, this article represents a commendable effort to advance the understanding of verb classification in Libras, laying important groundwork for future morphological and semantic investigations.


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