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Title:
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Contextual Variables
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Author:
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Luisa MartÃ
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Email:
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click here to access email
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Homepage:
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http://uit.no/castl/2400/49
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Degree Awarded:
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University of Connecticut
, Department of Linguistics
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Degree Date:
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2003
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Linguistic Subfield(s):
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Pragmatics
Semantics
Syntax
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Subject Language(s):
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Chinese, Mandarin
English
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Director(s):
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Yael Sharvit
Sigrid Beck
William Snyder
Howard Lasnik
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Abstract:
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The hypothesis pursued in this dissertation is that contextual variables (C) of the kind assumed for quantifiers like every or only are pronouns. One major advantage of taking this position is that if the behavior of C reduces to the behavior of pronouns, no new machinery needs to be added to the grammar in order to deal with C.
The C of quantificational expressions like every or no can be bound and is subject to the kinds of constraints that bound pronouns are subject to. In particular, C is subject to WCO in English and Chinese. In addition, whenever we find exceptions to WCO with pronouns in English, we find the same exceptions with C. The distribution of Chinese bound pronouns is more constrained than in English, and the distribution of C in Chinese is also more constrained.
As for free instances of C, I argue against analyses of association with focus that postulate non-pronoun-like constraints on C, since such analyses force a departure from the hypothesis that C is a pronoun. In the alternative analysis of association with focus proposed here, the burden of explanation is shifted to constraints on (implicit) discourse structure (Roberts (1996/1998), to which I add a principle based on maximal informativity. These constraints narrow down the kinds of contexts where sentences are felicitous. It is because of properties of the contexts in which sentences with only are felicitous that association-with-focus readings obtain: they contain only one suitable antecedent for the contextual variable of only. The same analysis is pursued for even, also and always, where certain difference between always and only (Beaver and Clark (2001, 2002a, b), Cohen (1999)) are explained. Maximal informativity finds additional support from facts independent of association. Other analyses of association, such as Rooth's (1992), are critically reviewed.
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