Generalized queries on probabilistic context-free grammars
Pynadath, D.V.; Wellman, M.P.
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Volume 20, Issue 1, Jan 1998 Page(s):65 - 77
Digital Object Identifier 10.1109/34.655650
Summary: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 developed
and 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. The network formalism also supports extensions to
encode various context sensitivities within the probabilistic dependency
structure
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