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David V. Pynadath, Michael P. Wellman, "Generalized Queries on Probabilistic ContextFree Grammars," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 6577, January, 1998.  
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@article{ 10.1109/34.655650, author = {David V. Pynadath and Michael P. Wellman}, title = {Generalized Queries on Probabilistic ContextFree Grammars}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {20}, number = {1}, issn = {01628828}, year = {1998}, pages = {6577}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.655650}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Generalized Queries on Probabilistic ContextFree Grammars IS  1 SN  01628828 SP65 EP77 EPD  6577 A1  David V. Pynadath, A1  Michael P. Wellman, PY  1998 KW  Probabilistic contextfree grammars KW  Bayesian networks. VL  20 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Abstract—Probabilistic contextfree grammars (PCFGs) provide a simple way to represent a particular class of distributions over sentences in a contextfree language. Efficient parsing algorithms for answering particular queries about a PCFG (i.e., calculating the probability of a given sentence, or finding the most likely parse) have been developed and applied to a variety of patternrecognition problems. We extend the class of queries that can be answered in several ways: (1) allowing missing tokens in a sentence or sentence fragment, (2) supporting queries about intermediate structure, such as the presence of particular nonterminals, and (3) flexible conditioning on a variety of types of evidence. Our method works by constructing a Bayesian network to represent the distribution of parse trees induced by a given PCFG. The network structure mirrors that of the chart in a standard parser, and is generated using a similar dynamicprogramming approach. We present an algorithm for constructing Bayesian networks from PCFGs, and show how queries or patterns of queries on the network correspond to interesting queries on PCFGs. The network formalism also supports extensions to encode various context sensitivities within the probabilistic dependency structure.
[1] R.C. Gonzalez and M.S. Thomason, Syntactic Pattern Recognition: An Introduction.Reading, Mass.: AddisonWesley, 1978.
[2] E. Charniak, Statistical Language Learning.Cambridge, Mass.: MIT Press, 1993.
[3] C.S. Wetherell, "Probabilistic Languages: A Review and Some Open Questions," Computing Surveys, vol. 12, no. 4, pp. 361379, 1980.
[4] P.A. Chou, "Recognition of Equations Using a TwoDimensional Stochastic ContextFree Grammar," Proc. SPIE: Visual Communications and Image Processing IV, Int'l Soc. Optical Eng., pp. 852863,Bellingham, Wash., 1989.
[5] H. Ney, "Stochastic Grammars and Pattern Recognition," Speech Recognition and Understanding, P. Laface and R. DeMori, eds., pp. 319344.Berlin: Springer, 1992.
[6] Y. Sakakibara, M. Brown, R.C. Underwood, I.S. Mian, and D. Haussler, "Stochastic ContextFree Grammars for Modeling RNA," Proc. 27th Hawaii Int'l Conf. System Sciences, pp. 284293, 1995.
[7] M. Vilain, "Getting Serious About Parsing Plans: A Grammatical Analysis of Plan Recognition," Proc. Eighth Nat'l Conf. Artificial Intelligence, pp. 190197, 1990.
[8] T. Briscoe and J. Carroll, "Generalized Probabilistic LR Parsing of Natural Language (Corpora) With UnificationBased Grammars," Computational Linguistics, vol. 19, no. 1, pp. 2559, Mar. 1993.
[9] J.E. Hopcroft and J.D. Ullman, Introduction to Automata Theory, Languages, and Computation.Reading, Mass.: AddisonWesley, 1979.
[10] F. Jelinek, J.D. Lafferty, and R.L. Mercer, "Basic Methods of Probabilistic Context Free Grammars," Speech Recognition and Understanding, P. Laface and R. DeMori, eds., pp. 345360.Berlin: Springer, 1992.
[11] J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.San Mateo, Calif.: Morgan Kaufmann, 1987.
[12] R.E. Neapolitan, Probabilistic Reasoning in Expert Systems: Theory and Algorithms.New York: John Wiley and Sons, 1990.
[13] F.V. Jensen, An Introduction to Bayesian Networks.New York: Springer, 1996.
[14] R. Dechter, "Bucket Elimination: A Unifying Framework for Probabilistic Inference," Proc. 12th Conf. Uncertainty in Artificial Intelligence, pp. 211219,San Francisco, 1996.
[15] E. Charniak and S.E. Shimony, "CostBased Abduction and MAP Explanation," Artificial Intelligence, vol. 66, pp. 345374, 1994.
[16] R. Dechter, "Topological Parameters for TimeSpace Tradeoff," Proc. 12th Conf. Uncertainty in Artificial Intelligence, pp. 220227,San Francisco, 1996.
[17] A. Darwiche and G. Provan, "Query DAGs: A Practical Paradigm for Implementing BeliefNetwork Inference," J. Artificial Intelligence Research, vol. 6, pp. 147176, 1997.
[18] E. Charniak and G. Carroll, "ContextSensitive Statistics for Improved Grammatical Language Models," Proc. 12th Nat'l Conf. Artificial Intelligence, pp. 728733,Menlo Park, Calif., 1994.
[19] D.V. Pynadath and M.P. Wellman, "Accounting for Context in Plan Recognition, With Application to Traffic Monitoring," Proc. 11th Conf. Uncertainty in Artificial Intelligence, pp. 472481,San Francisco, 1995.
[20] E. Black, F. Jelinek, J. Lafferty, D.M. Magerman, R. Mercer, and S. Roukos, "Towards HistoryBased Grammars: Using Richer Models for Probabilistic Parsing," Proc. Fifth DARPA Speech and Natural Language Workshop, M. Marcus, ed., pp. 3137, Feb. 1992.
[21] D.M. Magerman and M.P. Marcus, "Pearl: A Probabilistic Chart Parser," Proc. Second Int'l Workshop on Parsing Technologies, pp. 193199, 1991.
[22] D. Koller, D. McAllester, and A. Pfeffer, "Effective Bayesian Inference for Stochastic Programs," Proc. 14th Nat'l Conf. Artificial Intelligence, pp. 740747,Menlo Park, Calif., 1997.