Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Context-sensitive statistics for improved grammatical language models
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Probabilistic Languages: A Review and Some Open Questions
ACM Computing Surveys (CSUR)
An efficient context-free parsing algorithm
Communications of the ACM
Generalized probabilistic LR parsing of natural language (Corpora) with unification-based grammars
Computational Linguistics - Special issue on using large corpora: I
Accounting for context in plan recognition, with application to traffic monitoring
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Probabilistic query models for transaction data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A glimpse of symbolic-statistical modeling by PRISM
Journal of Intelligent Information Systems
Inside-outside probability computation for belief propagation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Effective Bayesian inference for stochastic programs
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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Probabilistic context-free grammars (PCFGs) provide a simple way to represent a particular class of distributions over sentences in a context-free 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 applied to a variety of pattern-recognition 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 dynamic-programming 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.