Querying parse trees of stochastic context-free grammars

Sara Cohen*, Benny Kimelfeld

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations


Stochastic context-free grammars (SCFGs) have long been recognized as useful for a large variety of tasks including natural language processing, morphological parsing, speech recognition, information extraction, Web-page wrapping and even analysis of RNA. A string and an SCFG jointly represent a probabilistic interpretation of the meaning of the string, in the form of a (possibly infinite) probability space of parse trees. The problem of evaluating a query over this probability space is considered under the conventional semantics of querying a probabilistic database. For general SCFGs, extremely simple queries may have results that include irrational probabilities. But, for a large subclass of SCFGs (that includes all the standard studied subclasses of SCFGs) and the language of tree-pattern queries with projection (and child/descendant edges), it is shown that query results have rational probabilities with a polynomial-size bit representation and, more importantly, an efficient query-evaluation algorithm is presented.

Original languageAmerican English
Title of host publicationDatabase Theory - ICDT 2010
Subtitle of host publication13th International Conference on Database Theory, Proceedings
Number of pages14
StatePublished - 2010
Event13th International Conference on Database Theory, ICDT'10 - Lausanne, Switzerland
Duration: 23 Mar 201025 Mar 2010

Publication series

NameACM International Conference Proceeding Series


Conference13th International Conference on Database Theory, ICDT'10


  • probabilistic databases
  • querying
  • stochastic context free grammars


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