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

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BIBLIOGRAPHY 190QUINLAN, J. ROSS, & RONALD L. RIVEST. 1989. Inferring decision trees using the minimum descriptionlength principle. Information and Computation 80.227–248.RABINER, L. R., & B. H. JUANG. 1986. An introduction to hidden Markov models. IEEE ASSP Magazine3.4–16.REBER, A. S. 1969. Implicit learning of artifical grammars. Journal of Verbal Learning and Verbal Behavior6.855–863.REDNER, RICHARD A., & HOMER F. WALKER. 1984. Mixture densities, maximum likelihood and the EMalgorithm. SIAM Review 26.195–239.REGIER, TERRY, 1992. The acquisition of lexical semantics for spatial terms: A connectionist model of perceptualcategorization. Berkeley, Ca.: Computer Science Division, University of California dissertation.RESNIK, PHILIP. 1992. Probabilistic tree-adjoining grammar as a framework for statistical natural languageprocessing. In Proceedings of the 14th InternationalConference on ComputationalLinguistics, 418–424,Nantes, France.RICHARDS, I. A., & C. GIBSON. 1945. English Through Pictures. New York: Washington Square Press.RILEY, MICHAEL D. 1991. A statistical model for generating pronunciation networks. In Proceedings IEEEConference on Acoustics, Speech and Signal Processing, volume 2, 737–740, Toronto.RISSANEN, JORMA. 1983. A universal prior for integers and estimation by minimum description length. TheAnnals of Statistics 11.416–431.RON, DANA, YORAM SINGER, & NAFTALI TISHBY. 1994. The power of amnesia. In Advances in NeuralInformation Processing Systems 6, ed. by Jack Cowan, Gerald Tesauro, & Joshua Alspector. San Mateo,CA: Morgan Kaufmann.SAKAKIBARA, YASUBUMI. 1990. Learning context-free grammars from structural data in polynomial time.Theoretical Computer Science 76.223–242.——, MICHAEL BROWN, RICHARD HUGHEY, I. SAIRA MIAN, KIMMEN SJÖLANDER, & REBECCA C. UN-DERWOOD DAVID HAUSSLER. 1994. The application of stochastic context-free grammars to folding,aligning and modeling homologous RNA sequences. Technical Report UCSC-CRL-94-14, Computerand Information Sciences, University of California, Santa Cruz, Ca.SCHABES, YVES, 1991. An inside-outside algorithm for estimating the parameters of a hidden stochasticcontext-free grammar based on Earley’s algorithm. Unpublished mss. Presented at the Second Workshopon Mathematics of Language, Tarritown, N.Y., May 1991.——. 1992. Stochastic lexicalized tree-adjoining grammars. In Proceedings of the 14th InternationalConference on Computational Linguistics, 426–433, Nantes, France.
BIBLIOGRAPHY 191SCHWARTZ, RICHARD, & YEN-LU CHOW. 1990. The -best algorithm: An efficient and exact procedure forfinding ¢ the most likely sentence hypotheses. In Proceedings IEEE Conference on Acoustics, Speechand Signal Processing, volume 1, 81–84, Albuquerque, NM.SHIEBER, STUART M. 1986. An Introduction to Unification-Based Approaches to Grammar. Number 4 inCSLI Lecture Note Series. Stanford, Ca.: Center for the Study of Language and Information.STOLCKE, ANDREAS. 1993. An efficient probabilistic context-free parsing algorithm that computes prefixprobabilities. Technical Report TR-93-065, International Computer Science Institute, Berkeley, CA. Toappear in Computational Linguistics.——. 1994. How to BOOGIE: a manual for Bayesian, object-oriented, grammar induction and estimation.Internal memo, International Computer Science Institute.——, & STEPHEN OMOHUNDRO. 1993. Hidden Markov model induction by Bayesian model merging. InAdvances in Neural Information Processing Systems 5, ed. by Stephen José Hanson, Jack D. Cowan, &C. Lee Giles, 11–18. San Mateo, CA: Morgan Kaufmann.——, & STEPHEN OMOHUNDRO. 1994a. Best-first model merging for hidden Markov model induction.Technical Report TR-94-003, International Computer Science Institute, Berkeley, CA.——, & STEPHEN OMOHUNDRO. 1994b. Inducing probabilistic grammars by Bayesian model merging. InGrammatical Inference and Applications. Second International Colloquium on Grammatical Inference,Alicante, Spain. Springer Verlag. To appear.——, & JONATHAN SEGAL. 1994. ¢ Precise -gram probabilities from stochastic context-free grammars. InProceedings of the 31th Annual Meeting of the Association for Computational Linguistics, 74–79, NewMexico State University, Las Cruces, NM.THOMASON, MICHAEL G., & ERIK GRANUM. 1986. Dynamic programming inference of Markov networksfrom finite set of sample strings. IEEE Transactions on Pattern Analysis and Machine Intelligence8.491–501.TOMITA, MASARU. 1982. Dynamic construction of finite automata from examples using hill-climbing. InProceedings of the 4th Annual Conference of the Cognitive Science Society, 105–108, Ann Arbor, Mich.——. 1986. Efficient Parsing for Natural Language. Boston: Kluwer Academic Publishers.UNDERWOOD, REBECCA CHRISTINE. 1994. Stochastic context-free grammars for modeling three spliceosomalsmall nuclear ribonucleic acids. Technical Report CRL-94-23, Computer and Information Sciences,University of California, Santa Cruz, Ca.VITERBI, A. 1967. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm.IEEE Transactions on Information Theory 13.260–269.
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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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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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CHAPTER 6. EFFICIENT PARSING WITH S
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Page 180 and 181: 168Chapter 7-grams from Stochastic
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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 198 and 199: BIBLIOGRAPHY 186DAGAN, IDO, FERNAND
Page 200 and 201: BIBLIOGRAPHY 188——, & ——. 1
Page 204: BIBLIOGRAPHY 192WALLACE, C. S., & P
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