Analogical Modeling
An exemplar-based approach to language
Editors
Analogical Modeling (AM) is an exemplar-based general theory of description that uses both neighbors and non-neighbors (under certain well-defined conditions of homogeneity) to predict language behavior. This book provides a basic introduction to AM, compares the theory with nearest-neighbor approaches, and discusses the most recent advances in the theory, including psycholinguistic evidence, applications to specific languages, the problem of categorization, and how AM relates to alternative approaches of language description (such as instance families, neural nets, connectionism, and optimality theory). The book closes with a thorough examination of the problem of the exponential explosion, an inherent difficulty in AM (and in fact all theories of language description). Quantum computing (based on quantum mechanics with its inherent simultaneity and reversibility) provides a precise and natural solution to the exponential explosion in AM. Finally, an extensive appendix provides three tutorials for running the AM computer program (available online).
[Human Cognitive Processing, 10] 2002. x, 416 pp.
Publishing status: Available
Published online on 21 October 2008
Published online on 21 October 2008
© John Benjamins Publishing Company
Table of Contents
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List of contributors | p. ix
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IntroductionRoyal Skousen | pp. 1–8
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Part I. The basics of Analogical Modeling
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1. An overview of Analogical ModelingRoyal Skousen | pp. 11–26
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2. Issues in Analogical ModelingRoyal Skousen | pp. 27–48
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Part II. Psycholinguistic evidence for Analogical Modeling
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3. Skousen’s analogical approach as an exemplar-based model of categorizationSteve Chandler | pp. 51–105
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Part III. Applications to specific languages
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4. Applying Analogical Modeling to the German pluralDouglas J. Wulf | pp. 109–122
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5. Testing Analogical Modeling: The /k/~Ø alternation in TurkishC. Anton Rytting | pp. 123–137
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Part IV. Comparing Analogical Modeling with TiMBL
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6. A comparison of two analogical models: Tilburg Memory-Based Learner versus Analogical ModelingDavid Eddington | pp. 141–155
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7. A comparison of Analogical Modeling to Memory-Based Language ProcessingWalter Daelemans | pp. 157–179
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8. Analogical hierarchy: Exemplar-based modeling of linkers in Dutch noun-noun compoundsAndrea Krott, Robert Schreuder and Harald Baayen | pp. 181–206
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Part V. Extending Analogical Modeling
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9. Expanding k -NN analogy with instance familiesAntal van den Bosch | pp. 209–223
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10. Version spaces, neural networks, and Analogical ModelingMike Mudrow | pp. 225–264
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11. Exemplar-driven analogy in Optimality TheoryJames Myers | pp. 265–300
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12. The hope for analogous categoriesChrister Johansson | pp. 301–316
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Part VI. Quantum computing and the exponential explosion
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13. Analogical Modeling and quantum computingRoyal Skousen | pp. 319–346
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Part VII. Appendix
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14. Data files for Analogical ModelingDeryle Lonsdale | pp. 349–363
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15. Running the Perl/C version of the Analogical Modeling programDilworth B. Parkinson | pp. 365–383
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16. Implementing the Analogical Modeling algorithmTheron Stanford | pp. 385–409
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Index | pp. 411–416
“It used to be a cliche that language users produce and understand new utterances on the basis of analogies they construct with previous linguistic experiences. A formal articulation of the notion of analogy was, however, lacking for a long time. Skousen's explicit formulation of analogy has triggered a resurgence of interest in analogy-based language processing. This book does a wonderful job of combining a tutorial on analogical modeling with a state-of-the-art overview of the field. It should be read by all who are interested in the interface between language, cognition, and computation.”
Rens Bod, University of Amsterdam
“Analogy — one of the most intuitive but elusive processes in language learning and change is here confronted directly, given a formal implementation and shown to be the force behind rule-like
behavior.”
behavior.”
Joan Bybee, University of New Mexico, Albuquerque
“The latest word on analogical modeling. This volume clearly distinguishes AM from both connectionism and symbolic rule systems.”
Bruce Derwing, University of Alberta, Edmonton
“This book succeeds extremely well in providing the reader with a tutorial on analogical modeling (AM) and the state-of-the art of the field, and is especially interesting for computational linguists.”
Remi van Trijp,
Sony Computer Science Laboratory Paris, in ICLA-review, February 2008
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2017. Chapter 10. Acquisition of phonological variables of a Flemish dialect by children raised in Standard Dutch. In Acquiring Sociolinguistic Variation [Studies in Language Variation, 20], ► pp. 267 ff.
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James P. Blevins & Juliette Blevins
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[no author supplied]
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Subjects
Main BIC Subject
CF: Linguistics
Main BISAC Subject
LAN009000: LANGUAGE ARTS & DISCIPLINES / Linguistics / General