Generalized queries on probabilistic context-free grammars

Conference · Tue Dec 31 04:00:00 EST 1996

OSTI ID:430816

Pynadath, D V; Wellman, M P ^[1]

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1. Univ. of Michigan, Ann Arbor, MI (United States)

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.

• OSTI ID: 430816
• Report Number(s): CONF-960876--; CNN: Grant F49620-94-1-0027
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
ALGORITHMS
ARTIFICIAL INTELLIGENCE
DYNAMIC PROGRAMMING
INFORMATION RETRIEVAL
NATURAL LANGUAGE
NETWORK ANALYSIS
PATTERN RECOGNITION