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

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184BibliographyAHO, ALFRED V., RAVI SETHI, & JEFFREY D. ULLMAN. 1986. Compilers: Principles, Techniques and Tools.Reading, Mass.: Addison-Wesley.——, & JEFFREY D. ULLMAN. 1972. The Theory of Parsing, Translation, and Compiling. Volume1: Parsing.Englewood Cliffs, N.J.: Prentice-Hall.ANGLUIN, D., & C. H. SMITH. 1983. Inductive inference: Theory and methods. ACM Computing Surveys15.237–269.BAHL, LALIT R., FREDERICK JELINEK, & ROBERT L. MERCER. 1983. A maximum likelihood approachto continuous speech recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence5.179–190.BAKER, JAMES K. 1979. Trainable grammars for speech recognition. In Speech Communication Papers forthe 97th Meeting of the Acoustical Society of America, ed. by Jared J. Wolf & Dennis H. Klatt, 547–550,MIT, Cambridge, Mass.BALDI, PIERRE, YVES CHAUVIN, TIM HUNKAPILLER, & MARCELLA A. MCCLURE. 1993. Hidden Markovmodels in molecular biology: New algorithms and applications. In Advances in Neural InformationProcessing Systems 5, ed. by Stephen José Hanson, Jack D. Cowan, & C. Lee Giles, 747–754. SanMateo, CA: Morgan Kaufmann.BAUM, LEONARD E., TED PETRIE, GEORGE SOULES, & NORMAN WEISS. 1970. A maximization technique occuringin the statistical analysis of probabilistic functions in Markov chains. The Annals of MathematicalStatistics 41.164–171.BELL, TIMOTHY C., JOHN G. CLEARY, & IAN H. WITTEN. 1990. Text Compression. Englewood Cliffs, N.J.:Prentice Hall.BOOTH, TAYLOR L., & RICHARD A. THOMPSON. 1973. Applying probability measures to abstract languages.IEEE Transactions on Computers C-22.442–450.BOURLARD, HERVÉ, & NELSON MORGAN. 1993. Connectionist Speech Recognition. A Hybrid Approach.Boston, Mass.: Kluwer Academic Publishers.
BIBLIOGRAPHY 185BRILL, ERIC. 1993. Automatic grammar induction and parsing free text: A transformation-based approach.In Proceedings ARPA Workshop on Human Language Technology, Plainsboro, N.J.BRISCOE, TED, & JOHN CARROLL. 1993. Generalized probabilistic LR parsing of natural language (corpora)with unification-based grammars. Computational Linguistics 19.25–59.BROWN, PETER F., VINCENT J. DELLA PIETRA, PETER V. DESOUZA, JENIFER C. LAI, & ROBERT L. MERCER.1992. ¢ Class-based -gram models of natural language. Computational Linguistics 18.467–479.BUNTINE, W. L. 1991. Theory refinement of Bayesian networks. In Seventh Conference on Uncertainty inArtificial Intelligence, Anaheim, CA.BUNTINE, WRAY. 1992. Learning classification trees. In Artificial Intelligence Frontiers in Statistics: AI andStatistics III, ed. by D. J. Hand. Chapman & Hall.CHEESEMAN, PETER, JAMES KELLY, MATTHEW SELF, JOHN STUTZ, WILL TAYLOR, & DON FREEMAN. 1988.AutoClass: A Bayesian classification system. In Proceedings of the 5th International Conference onMachine Learning, 54–64, University of Michigan, Ann Arbor, Mich.CHEN, FRANCINE R. 1990. Identification of contextual factors for pronunciation networks. In ProceedingsIEEE Conference on Acoustics, Speech and Signal Processing, volume 2, 753–756, Albuquerque, NM.CHURCH, KENNETH W., & WILLIAM A. GALE. 1991. A comparison of the enhanced Good-Turing and deletedestimation methods for estimating probabilities of English bigrams. Computer Speech and Language5.19–54.CLEEREMANS, AXEL, 1991. Mechanisms of Implicit Learning. A Parallel Distributed Processing Modelof Sequence Acquisition. Pittsburgh, Pa.: Department of Psychology, Carnegie Mellon Universitydissertation.COOK, CRAIG M., AZRIEL ROSENFELD, & ALAN R. ARONSON. 1976. Grammatical inference by hill climbing.Information Sciences 10.59–80.COOPER, GREGORY F., & EDWARD HERSKOVITS. 1992. A Bayesian method for the induction of probabilisticnetworks from data. Machine Learning 9.309–347.CORAZZA, ANNA, RENATO DE MORI, ROBERTO GRETTER, & GIORGIO SATTA. 1991. Computation ofprobabilitiesfor an island-drivenparser. IEEE Transactions on Pattern Analysis and Machine Intelligence13.936–950.COVER, THOMAS M., & JOY A. THOMAS. 1991. Elements of Information Theory. New York: John Wiley andSons, Inc.COX, R. T. 1946. Probability, frequency and reasonable expectation. American Journal of Physics 14.
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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 18As more da
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CHAPTER 2. FOUNDATIONS 20Global mod
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CHAPTER 2. FOUNDATIONS 22¡An expli
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CHAPTER 3. HIDDEN MARKOV MODELS 520
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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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,1makingandCHAPTER 5. PROBABILISTIC
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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 150 and 151: ) The probabilistic unit-production
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Page 162 and 163: description. Again, we ignore this
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Page 170 and 171: are then summed over all nontermina
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Page 176 and 177: CHAPTER 6. EFFICIENT PARSING WITH S
Page 178 and 179: = ¸Let t,t) ¸ .V=i£,t,t,6666yyyy
Page 180 and 181: 168Chapter 7-grams from Stochastic
Page 182 and 183: CHAPTER 7. -GRAMS FROM STOCHASTIC
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Page 186 and 187: -grams CCHAPTER 7. -GRAMS FROM ST
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Page 190 and 191: ,,CHAPTER 7. -GRAMS FROM STOCHASTI
Page 192 and 193: Consider the following problem: sta
Page 194 and 195: CHAPTER 8. FUTURE DIRECTIONS 1828.2
Page 198 and 199: BIBLIOGRAPHY 186DAGAN, IDO, FERNAND
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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