The Semantics of Degree

Cresswell, M. J.

doi:10.1016/b978-0-12-545850-4.50015-7

Cited by 586 publications

(346 citation statements)

References 11 publications

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“…These models tend to be strongly interrelated, see Dubois and Prade (1997), and for graded adjectives a common formulation is in terms of an uncertain threshold value defined on a particular measurement scale (Cresswell 1976). Consider, for example, the adjective short defined on a height scale corresponding to the positive real numbers.…”

Section: The Uncertain Threshold Model Of Vaguenessmentioning

confidence: 99%

Vagueness and Aggregation in Multiple Sender Channels

Lawry

James

2017

Erkenn

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Vagueness is an extremely common feature of natural language, but does it actually play a positive, efficiency enhancing, role in communication? Adopting a probabilistic interpretation of vague terms, we propose that vagueness might act as a source of randomness when deciding what to assert. In this context we investigate the efficacy of multiple sender channels in which senders choose assertions stochastically according to vague definitions of the relevant words, and a receiver then aggregates the different signals. These vague channels are then compared with Boolean channels in which assertions are selected deterministically based on classical (crisp) definitions. We show that given a sufficient number of senders, a linear stochastic channel outperforms Boolean channels when performance is measured by the expected squared error between the actual value described by the senders and the receiver's estimate of it based on the signals they receive. The number of senders required for vague channels to be at least as accurate as Boolean channels is shown to be a decreasing function of the size of the language i.e. the number of description labels available to the senders. Vague channels are then shown to be robust to transmission error provided the error rate is not too large. In addition, we investigate the behaviour of both Boolean and vague channels for a parametrised family of distributions on the input values. Finally, we consider optimal vague channels assuming a fixed number of senders and show that, provided there are more than two senders, a vague channel can be found that outperforms the optimal Boolean channel. In this context, we show that for channels with relatively low numbers of senders S-curve production functions are optimal.

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Section: The Uncertain Threshold Model Of Vaguenessmentioning

confidence: 99%

Vagueness and Aggregation in Multiple Sender Channels

Lawry

James

2017

Erkenn

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“…The positive form of an adjective (which does not combine with any explicit degree morphology) combines with an aphonic morpheme to produce something like the semantic value described above. See, for example, (Cresswell, 1977) and (Kennedy, 1999).…”

Section: True Cross-contextual Reports and Infelicitous Denials Of Comentioning

confidence: 99%

Elaboration and intuitions of disagreement

Davies

2016

Philos Stud

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Mark Richard argues for truth-relativism about claims made using gradable adjectives. He argues that truth-relativism is the best explanation of two kinds of linguistic data, which I call: true cross-contextual reports and infelicitous denials of conflict. Richard claims that such data are generated by an example that he discusses at length. However, the consensus is that these linguistic data are illusory because they vanish when elaborations are added to examples of the same kind as Richard's original. In this paper I defend the reality of Richard's data. I show that, in trying to make their point, Richard's critics have focused upon examples that are similar in some respects to Richard's original but which lack a crucial feature of that original. When we ensure that this feature is in place, elaborations which make the data vanish are not possible. Richard's critics therefore fail to show that the data generated by Richard's original example are illusory.

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“…Following an influential line of work, we treat gradable adjectives as relations between individuals and degrees, namely type d, e,t (Cresswell 1976;Heim 2001;Kennedy & McNally 2005). A denotation for tall is thus given in (11):…”

Section: Lower-bound Scalementioning

confidence: 99%

“…In the case of gradable adjectives, PREC applies to the positive form, i.e., after the application of POS, which introduces the standard (see, e.g., Cresswell 1976;von Stechow 1984;Kennedy & McNally 2005). Morzycki's denotation for metalinguistic more is then given in (17).…”

Section: Metalinguistic Degree Morphemes and Scales Of Precisionmentioning

confidence: 99%

A new metalinguistic degree morpheme

Bochnak

Csipak²

2015

SALT

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In this paper, we discuss and analyze . . . ish/-ish in three of its uses: as a modifier of gradable adjectives; as a clause-final particle that hedges on a speaker's degree of commitment to a proposition; and as a general precision-regulator. In each case, . . . ish/-ish makes reference to a degree that is slightly less than the standard for the constituent it applies to. We propose that proposition-modifying . . . ish belongs to the class of metalinguistic degree morphology alongside metalinguistic comparisons, which have received recent attention in the literature (e.g., Giannakidou & Yoon 2011; Morzycki 2011). We argue for a unified analysis of . . . ish/-ish as a degree modifier, where the relevant degree variable can be provided lexically, or through a type-shifting operation that makes available a degree of precision in the sense of Morzycki (2011). This study has implications for research on the semantics of metalinguistic degree morphology, imprecision, speaker-oriented phenomena, and the role of subjectification in semantic change.

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The Semantics of Degree

Cited by 586 publications

References 11 publications

Vagueness and Aggregation in Multiple Sender Channels

Vagueness and Aggregation in Multiple Sender Channels

Elaboration and intuitions of disagreement

A new metalinguistic degree morpheme

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