The dissertation of Andreas Stolcke is approved: University of ...

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BIBLIOGRAPHY 186DAGAN, IDO, FERNANDO PEREIRA, & LILLIAN LEE. 1994. Similarity-based estimation of word coocurrenceprobabilities. In Proceedings of the 31th Annual Meeting of the Association for ComputationalLinguistics, New Mexico State University, Las Cruces, NM.DE SAUSSURE, FERDINAND. 1916. Cours de linguistique generale. Paris: Payot.DEMPSTER, A. P., N. M. LAIRD, & D. B. RUBIN. 1977. Maximum likelihood from incomplete data via theEM algorithm. Journal of the Royal Statistical Society, Series B 34.1–38.EARLEY, JAY. 1970. An efficient context-free parsing algorithm. Communications of the ACM 6.451–455.EVANS, T. G. 1971. Grammatical inference techniques in pattern analysis. In Software engineering, ed. byJ. Tou, 183–202. New York: Academic Press.FASS, LEONA F. 1983. Learning context-free languages from their structured sentences. ACM SIGACT News15.24–35.FELDMAN, J., G. LAKOFF, D. BAILEY, S. NARAYANAN, T. REGIER, & A. STOLCKE. ¨ 1994. 0—the first fouryears. AI Review 8. Special issue on Integration of Natural Language and Vision Processing, to appear.FELDMAN, JEROME A., GEORGE LAKOFF, ANDREAS STOLCKE, & SUSAN HOLLBACH WEBER. 1990. Miniaturelanguage acquisition: A touchstone for cognitive science. In Proceedings of the 12th Annual Conferenceof the Cognitive Science Society, 686–693, MIT, Cambridge, Mass.FILLMORE, CHARLES J. 1988. The mechanisms of “Construction Grammar”. In Proceedings of the FourteenthAnnual Meeting of the Berkeley Linguistics Society, ed. by Shelley Axmaker, Annie Jaisser, & HelenSingmeister, 35–55, Berkeley, Ca.FUJISAKI, T., F. JELINEK, J. COCKE, E. BLACK, & T. NISHINO. 1991. A probabilistic parsing method forsentence disambiguation. In Current Issues in Parsing Technology, ed. by Masaru Tomita, chapter 10,139–152. Boston: Kluwer Academic Publishers.GAROFOLO, J. S., 1988. Getting Started with the DARPA TIMIT CD-ROM: an Acoustic Phonetic ContinuousSpeech Database. National Institute of Standards and Technology (NIST), Gaithersburgh, Maryland.GAUVAIN, JEAN-LUC, & CHIN-HIN LEE. 1991. Bayesian learning of Gaussian mixture densities for hiddenMarkov models. In Proceedings DARPA Speech and Natural Language Processing Workshop, 271–277.Pacific Grove, CA: Defence Advanced Research Projects Agency, Information Science and TechnologyOffice.GAZDAR, GERALD, E. KLEIN, G. K. PULLUM, & I. A. SAG. 1985. Generalized Phrase Structure Grammar.Cambridge, Mass.: Harvard University Press.GEMAN, STUART, ELIE BIENENSTOCK, & RENÉ DOURSAT. 1992. Neural networks and the bias/variancedilemma. Neural Computation 4.1–58.
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The dissertation of Andreas Stolcke
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Bayesian Learning of Probabilistic
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iAcknowledgmentsLife and work in Be
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iiiContentsList of FiguresList of T
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CONTENTSv4.5.4 Summary and Discussi
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CHAPTER 1. INTRODUCTION 2Instance-b
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CHAPTER 1. INTRODUCTION 4A.0.830.33
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CHAPTER 1. INTRODUCTION 6the ¨ 0 l
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CHAPTER 2. FOUNDATIONS 20Global mod
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correlation between initial and fin
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CHAPTER 3. HIDDEN MARKOV MODELS 62b
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CHAPTER 3. HIDDEN MARKOV MODELS 64t
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CHAPTER 3. HIDDEN MARKOV MODELS 66t
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CHAPTER 3. HIDDEN MARKOV MODELS 68s
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CHAPTER 3. HIDDEN MARKOV MODELS 74b
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domain. 3 In short, we will leave o
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104Chapter 5Probabilistic Attribute
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CHAPTER 5. PROBABILISTIC ATTRIBUTE
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122Chapter 6Efficient parsing with
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1z1CHAPTER 6. EFFICIENT PARSING WIT
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and each state in set ( -ÏH ):6)
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NPDetVTVI P CHAPTER 6. EFFICIENT PA
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CHAPTER 6. EFFICIENT PARSING WITH S
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) In particular, the string probabi
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Page 180 and 181: 168Chapter 7-grams from Stochastic
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Page 186 and 187: -grams CCHAPTER 7. -GRAMS FROM ST
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Page 192 and 193: Consider the following problem: sta
Page 194 and 195: CHAPTER 8. FUTURE DIRECTIONS 1828.2
Page 196 and 197: 184BibliographyAHO, ALFRED V., RAVI
Page 200 and 201: BIBLIOGRAPHY 188——, & ——. 1
Page 202 and 203: BIBLIOGRAPHY 190QUINLAN, J. ROSS, &
Page 204: BIBLIOGRAPHY 192WALLACE, C. S., & P
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