Generalized queries on probabilistic context-free grammars
- 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
Similar Records
Generalized augmented transition network grammars for generation from semantic networks
Note on parallel parsing