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). uantum 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). Table of ContentsList of contributors ix Introduction Royal Skousen 1 I. The basics of Analogical Modeling 1. An overview of Analogical Modeling Royal Skousen 11 2. Issues in Analogical Modeling Royal Skousen 27 II. Psycholinguistic evidence for Analogical Modeling 3. Skousen’s analogical approach as an exemplar-based model of categorization Steve Chandler 51 III. Applications to specific languages 4. Applying Analogical Modeling to the German plural Douglas J. Wulf 109 5. Testing Analogical Modeling: The /k/ ~Ø alternation in Turkish C. Anton Rytting 123 IV. Comparing Analogical Modeling with TiMBL 6. A comparison of two analogical models: Tilburg Memory-Based Learner versus Analogical Modeling David Eddington 141 7. A comparison of Analogical Modeling to Memory-Based Language Processing Walter Daelemans 157 8. Analogical hierarchy: Exemplar-based modeling of linkers in Dutch noun-noun compounds Andrea Krott, Robert Schreuder and R. Harald Baayen 181 V. Extending Analogical Modeling 9. Expanding k-NN analogy with instance families Antal van den Bosch 209 10. Version spaces, neural networks, and Analogical Modeling Mike Mudrow 225 11. Exemplar-driven analogy in Optimality Theory James Myers 265 12. The hope for analogous categories Christer Johansson 301 VI. uantum computing and the exponential explosion 13. Analogical Modeling and quantum computing Royal Skousen 319 VII. Appendix 14. Data files for Analogical Modeling Deryle Lonsdale 349 15. Running the Perl/C version of the Analogical Modeling program Dilworth B. Parkinson 365 16. Implementing the Analogical Modeling algorithm Theron Stanford 385