Vagueness, Gradability and Typicality: The Interpretation of Adjectives and Nouns

Part I: Data and Theories, An Overview

1 INTRODUCTION
1.1 Basic notions and goals
1.2 Chapter 2: Vagueness, gradability and typicality: two sets of facts
1.3 Chapter 3: The linguistic perspective on vagueness, gradability and typicality
1.4 Chapter 4: The psychological perspective on vagueness, gradability and typicality
1.5 Part 2: The new proposal
1.5.1 Chapters 5-6: Partial information about graded structures
1.5.2 Chapter 7: A typology of predicates by the type of their degree function
1.5.3 Chapter 8: The connections between vagueness and gradability
1.5.4 Chapter 9: Polarity effects
1.5.5 Chapter 10: Conclusions and beyond

2 THE LINGUISTIC AND PSYCHOLOGICAL PERSPECTIVES
2.1 The linguistic data
2.1.1 Vagueness
2.1.2 Gradability
2.1.3 Positive versus negative predicates: polarity effects
2.2 The psychological data
2.2.1 Vagueness in nouns
2.2.2 Entity orderings in nouns
2.2.3 Dimensions in nouns
2.2.4 Typicality and denotation membership
2.2.5 Typicality and learning
2.2.6 The productive nature of typicality effects
2.3 Intermediate conclusions and one last piece of data

3 AN OVERVIEW OF LINGUISTIC THEORIES
3.1 The representation of vagueness
3.2 The analysis of gradability
3.2.1 The connection between vagueness and gradability
3.2.2 The nature of the degrees: The ordinal scale versus interval scale controversy
3.2.3 Polarity
3.3 Typicality in linguistic theories
3.3.1 Background: multi-valued semantics
3.3.2 Supermodel theory: Kamp and Partee (1995)
3.3.3 Problems with the supermodel theory

4 AN OVERVIEW OF PSYCHOLOGICAL THEORIES
4.1 Prototype theory
4.1.1 Dimension sets, mean distance and similarity
4.1.2 Standard-based categorization
4.1.3 Contrast-based-categorization
4.1.4 The main problem of prototype models: linear separability
4.2 Exemplar theory
4.2.1 Exemplar-based similarity
4.2.2 The results of exemplar extension
4.2.3 The disadvantage of eliminating summary representations
4.2.4 Are exemplar-based and prototype-based predictions inconsistent?
4.3 Objections to truth conditional theories
4.3.1 The conjunction and sub-type effects and failures of intersection inferences
4.3.2 A composite-prototype representation
4.3.3 Constituent-based predictions
4.4 The representation of information about prototypes
4.4.1 The probabilistic criterion
4.4.2 The knowledge criterion
4.5 Apparent dissociations between judgments of typicality and membership likelihood
4.6 Conclusions

Part II: A Comprehensive Semantic Analysis

5 INFORMATION STRUCTURE WITH DEGREES AND DIMENSIONS
5.1 Modeling partial information about degrees and dimensions
5.2 The inhabitants of vagueness models: Representing vagueness and ignorance
5.3 Numerical degree functions
5.4 Negative predicates: the quantity metaphor and transformation values
5.5 Dimensions and dimension sets
5.6 Nouns, adjectives, and more
5.7 The degree function of multidimensional adjectives

6 PARTIAL INFORMATION ABOUT GRADABILITY AND TYPICALITY
6.1 The syntax of the language
6.2 Vagueness models with degree functions
6.2.1 A degree-ontology: D and Df
6.2.3 The extension assigning function Extension
6.2.4 The set of assignment functions G
6.2.5 Semantic values relative to a context t in T and an assignment g in G
6.2.6 Super semantic values relative to a context c in C and an assignment g in G
6.2.7 The additional elements in the interpretation of predicates, I
6.2.8 The superelements of the interpretation of predicates, I, relative to c and g

7 A DEGREE-FUNCTION BASED TYPOLOGY OF PREDICATES
7.1 Standard based categorization
7.2 Membership standards as domain-based
7.2.1 The connections between standards and domains
7.2.2 Domain dependent standards of absolute predicates
7.2.3 The standards of nominal concepts
7.3 Negative versus positive adjectives
7.4 Nominal concepts: a prototype theory
7.4.1 Nominal concepts are similarity-based
7.4.2 The representation of partial and context-dependent information about