Dependent Plurality and the Theory of Scalar Implicatures: Remarks on Zweig 2009

IF 1.6 3区 工程技术 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Journal of Biomedical Semantics Pub Date : 2020-08-13 DOI:10.1093/jos/ffaa004
N. Ivlieva
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引用次数: 2

Abstract

Following a recent discussion in Fox & Spector 2018, this paper provides an argument for a particular view of the theory of scalar implicatures and exhaustification where exhaustification is only allowed if it alters the overall sentence meaning without weakening it. I show that this idea is helpful to make sense of the so-called dependent plural interpretations, addressed within the theory of scalar implicatures in Zweig 2009 (see also Zweig 2008). Even though Zweig’s account is based on insightful and plausible assumptions (most crucially, the idea that the multiplicity component of the meaning of plurals is a scalar implicature), it ultimately fails to derive dependent plural readings. The main reason for this is the use of the Strongest Candidate Principle of Chierchia 2006 that happens to filter out the needed interpretation. Replacing the Strongest Candidate Principle with a weaker constraint on exhaustification along the lines of Fox & Spector 2018 resolves the issue, while keeping most of Zweig’s insights intact.
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依赖复数与标量蕴涵理论:关于Zweig 2009的评论
在Fox & Spector 2018最近的一次讨论之后,本文为标量含义和耗尽理论的特定观点提供了一个论据,其中耗尽只有在改变整个句子的意义而不削弱它时才被允许。我表明,这个想法有助于理解所谓的依赖复数解释,在Zweig 2009的标量蕴涵理论中解决(另见Zweig 2008)。尽管茨威格的描述是基于深刻而可信的假设(最关键的是,复数意义的多重成分是标量含意的想法),但它最终未能推导出依赖的复数解读。这样做的主要原因是使用了Chierchia 2006的最强候选原则,该原则恰好过滤掉了所需的解释。按照Fox & Spector 2018的思路,用对耗竭的较弱约束取代最强候选原则解决了这个问题,同时保留了茨威格的大部分见解。
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来源期刊
Journal of Biomedical Semantics
Journal of Biomedical Semantics MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
4.20
自引率
5.30%
发文量
28
审稿时长
30 weeks
期刊介绍: Journal of Biomedical Semantics addresses issues of semantic enrichment and semantic processing in the biomedical domain. The scope of the journal covers two main areas: Infrastructure for biomedical semantics: focusing on semantic resources and repositories, meta-data management and resource description, knowledge representation and semantic frameworks, the Biomedical Semantic Web, and semantic interoperability. Semantic mining, annotation, and analysis: focusing on approaches and applications of semantic resources; and tools for investigation, reasoning, prediction, and discoveries in biomedicine.
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