Computational models of referring: a study in cognitive science
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Massachusetts
The MIT Press
[2016]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | x, 339 Seiten Illustrationen 24 cm |
| ISBN: | 9780262034555 |
Internformat
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| 245 | 1 | 0 | |a Computational models of referring |b a study in cognitive science |c Kees van Deemter |
| 264 | 1 | |a Cambridge, Massachusetts |b The MIT Press |c [2016] | |
| 264 | 4 | |c © 2016 | |
| 300 | |a x, 339 Seiten |b Illustrationen |c 24 cm | ||
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| 505 | 8 | |a Includes bibliographical references and index | |
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| 650 | 4 | |a Presupposition (Logic) | |
| 650 | 4 | |a Computational linguistics | |
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Datensatz im Suchindex
| _version_ | 1805084132902961152 |
|---|---|
| adam_text |
Contents
Preface 1
I FIRST PART: SETTING THE STAGE 5
1 Aims and Scope of This Book 7
1.1 Aims and Main Thesis 8
1.2 Reference in Practical Applications of Computing 12
1.3 Computational Models of Reference Production 14
1.4 Determining the Information Content of an RE 16
1.5 Focus on Speakers or Hearers? 18
1.6 Referring in One Shot 19
1.7 A Perspective on Reference: Information Sharing 21
1.8 Summary of the Chapter 23
2 Theories of Reference 25
2.1 What Makes a Referring Expression? 25
2.2 Knowing What Something Is 28
2.3 Denotation and Connotation 30
2.4 The Russell-Strawson Debate 32
2.5 Intensional Contexts 35
2.6 Attributive Descriptions and Misdescriptions 37
2.7 Proper Names 39
2.8 The Gricean Maxims and Relevance Theory 41
2.9 Summary of the Chapter 43
3 The Psychology of Reference Production 45
3.1 Common Ground 45
3.2 Audience Design and the Egocentricity Debate 50
3.3 Rationality and the Gricean Maxims 55
3.4 intrinsic Preference for Certain Attributes 60
3.5 Comparing Preference with Discrimination 63
3.6 Insights from Dialogue 66
3.7 Ecological Validity of Experiments 68
3.8 Summary of the Chapter 69
II SECOND PART: SOLVING THE CLASSIC REG PROBLEM 71
4 Getting Computers to Refer 73
4.1 Computational Pre-history of REG 73
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Contents
4.2 The California School
4.3 The Classic REG Task
4.4 Assumptions Behind the Classic REG Task
4.5 Exploring the Gricean Angle Computationally
4.6 The Incremental Algorithm
4.7 Logical (In)completeness
4.8 Computational Tractability of REG Algorithms
4.9 Salience
4.10 Summary of the Chapter
Testing REG Algorithms: The TUNA Experiment
5.1 Why the tuna Experiment?
5.2 How to Test a REG Algorithm?
5.3 The TUNA Corpus and Its Annotation
5.4 Analysis of the Furniture Corpus
5.5 Analysis of the People Corpus
5.6 Modelling a Plurality of Speakers
5.7 Lessons from the TUNA Experiment
5.8 Lessons from the TUNA Evaluation Challenges
5.9 A Note on Alternative Metrics
5.10 Summary of the Chapter
Probabilistic and Other Alternatives to the Classic REG Algorithms
6.1 Variations in Language Production
6.2 Bayesian Models of Reference
6.3 Probabilistic Referential Overspecification: the PRO Algorithm
6.4 Constraint Satisfaction for REG
6.5 Krahmer et al.’s Cost-Based Approach
6.6 Appelt’s Heirs: Reference as Part of a Wider Problem
6.7 Summary of the Chapter
THIRD PART: GENERATING A WIDER CLASS OF RES
First Extension: Using Proper Names
7.1 Why Have Proper Names Been Neglected in REG?
7.2 Incorporating Proper Names into REG
7.3 Reifying Properties
ix
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Contents
7.4 Challenges for REG Posed by Proper Names
7.5 Summary of the Chapter
Second Extension: Referring to Sets
8.1 Purely Conjunctive References to Sets
8.2 Negation and Disjunction
8.3 Satellite Sets and Their Use in reg
8.4 Generating Boolean Logical Forms Incrementally
8.5 Optimization of Generated res
8.6 Issues Raised by the Algorithms Proposed
8.7 Lexical Coherence in Conjoined REs
8.8 Avoiding Surface Ambiguities
8.9 Beyond Sets of Objects
8.10 Summary of the Chapter
Third Extension: Using Gradable Properties
9.1 The Semantics of Vague Descriptions
9.2 Pragmatic Constraints on What Can Be Said
9.3 Empirical Grounding
9.4 Computational Generation of Vague Descriptions
9.5 Puzzles for Incremental Content Determination
9.6 A Case Study: Real-World Objects and Their Sizes
9.7 Can We Ever Be Clear? Salience as a Gradable Property
9.8 Summary of the Chapter
Fourth Extension: Exploiting Modern Knowledge Representation
10.1 Knowledge Representation and REG
10.2 Description Logic: a Primer
10.3 Applying Description Logic to Familiar REG Problems
10.4 Exploiting the Full Power of dl
10.5 Using S720XQ+ to Generate Complex REs
10.6 Rethinking REG: Using Shared Knowledge That Is Not Atomic
10.7 Why Study the Generation of Logically Complex REs?
10.8 Summary of the Chapter
The Question of Referability
1 LI Revisiting the Logical Completeness of REG
11.2 Limitations of STIOXQ + and the GROWL Algorithm
x Contents
11.3 Even More Expressive Algorithms? 259
11.4 Summary of the Chapter 260
IV FOURTH PART: GENERALIZING REFERENCE GENERATION 261
12 First Challenge: Large Domains 263
13 Second Challenge: Breakdown of Common Knowledge 273
14 Third Challenge: Approximate Reference 281
15 Fourth Challenge: Going Beyond Identification 285
Summary of Part IV: Complexities of Information Sharing 292
V EPILOGUE 293
16 Epilogue 295
16.1 REG Algorithms as Cognitive Models 296
16.2 The Gricean Maxims and the Principle of Intrinsic Preference 300
16.3 Future Research: The Way Ahead 304
Frequently Occurring Terms and Abbreviations 311
Bibliography 313
Index 333
To communicate, speakers need to make it dear what they are talking about. The act of referring, which
anchors words to things, is a fundamental aspect of language. In this book, Kees van Deemter shows that
computational models of reference offer attractive tools for capturing the complexity of referring.
Indeed, the models van Deemter presents cover many issues beyond the basic idea of referring to an
object, including reference to sets, approximate descriptions, descriptions produced under uncertainty
concerning the hearer’s knowledge, and descriptions that aim to inform or influence the hearer.
The book, which can be read as a case study in cognitive science, draws on perspectives from across the
cognitive sciences, including philosophy, experimental psychology, formal logic, and computer science.
Van Deemter advocates a combination of computational modeling and careful experimentation as the pre-
ferred method for expanding these insights. He then shows this method in action, covering a range of
algorithms and a variety of methods for testing them. He shows that the method allows us to model logi-
cally complicated referring expressions, and demonstrates how we can gain an understanding of reference
in situations where the speaker's knowledge is difficult to assess or where the referent resists exact defi-
nition. Finally, he proposes a program of research that addresses the open questions that remain in this
area, arguing that this program can significantly enhance our understanding of human communication. |
| any_adam_object | 1 |
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| discipline | Informatik |
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| spelling | Deemter, Kees van Verfasser (DE-588)142365289 aut Computational models of referring a study in cognitive science Kees van Deemter Cambridge, Massachusetts The MIT Press [2016] © 2016 x, 339 Seiten Illustrationen 24 cm txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Reference (Linguistics) Presupposition (Logic) Computational linguistics Computational linguistics fast Presupposition (Logic) fast Reference (Linguistics) fast Referenz Linguistik (DE-588)4137933-0 gnd rswk-swf Computersimulation (DE-588)4148259-1 gnd rswk-swf Sprachdaten (DE-588)4312652-2 gnd rswk-swf Computerlinguistik (DE-588)4035843-4 gnd rswk-swf Computersimulation (DE-588)4148259-1 s Computerlinguistik (DE-588)4035843-4 s Referenz Linguistik (DE-588)4137933-0 s DE-604 Sprachdaten (DE-588)4312652-2 s 1\p DE-604 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029044204&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029044204&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Deemter, Kees van Computational models of referring a study in cognitive science Includes bibliographical references and index Reference (Linguistics) Presupposition (Logic) Computational linguistics Computational linguistics fast Presupposition (Logic) fast Reference (Linguistics) fast Referenz Linguistik (DE-588)4137933-0 gnd Computersimulation (DE-588)4148259-1 gnd Sprachdaten (DE-588)4312652-2 gnd Computerlinguistik (DE-588)4035843-4 gnd |
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| title | Computational models of referring a study in cognitive science |
| title_auth | Computational models of referring a study in cognitive science |
| title_exact_search | Computational models of referring a study in cognitive science |
| title_full | Computational models of referring a study in cognitive science Kees van Deemter |
| title_fullStr | Computational models of referring a study in cognitive science Kees van Deemter |
| title_full_unstemmed | Computational models of referring a study in cognitive science Kees van Deemter |
| title_short | Computational models of referring |
| title_sort | computational models of referring a study in cognitive science |
| title_sub | a study in cognitive science |
| topic | Reference (Linguistics) Presupposition (Logic) Computational linguistics Computational linguistics fast Presupposition (Logic) fast Reference (Linguistics) fast Referenz Linguistik (DE-588)4137933-0 gnd Computersimulation (DE-588)4148259-1 gnd Sprachdaten (DE-588)4312652-2 gnd Computerlinguistik (DE-588)4035843-4 gnd |
| topic_facet | Reference (Linguistics) Presupposition (Logic) Computational linguistics Referenz Linguistik Computersimulation Sprachdaten Computerlinguistik |
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