Pearl: A Probabilistic Chart Parser

David M. Magerman, Mitchell P. Marcus


Abstract
This paper describes a natural language parsing algorithm for unrestricted text which uses a probability-based scoring function to select the “best” parse of a sentence. The parser, Pearl, is a time-asynchronous bottom-up chart parser with Earley-type top-down prediction which pursues the highest-scoring theory in the chart, where the score of a theory represents the extent to which the context of the sentence predicts that interpretation. This parser differs from previous attempts at stochastic parsers in that it uses a richer form of conditional probabilities based on context to predict likelihood. Pearl also provides a framework for incorporating the results of previous work in part-of-speech assignment, unknown word models, and other probabilistic models of linguistic features into one parsing tool, interleaving these techniques instead of using the traditional pipeline architecture. In preliminary tests, Pearl has been successful at resolving part-of-speech and word (in speech processing) ambiguity, determining categories for unknown words, and selecting correct parses first using a very loosely fitting covering grammar.
Anthology ID:
1991.iwpt-1.22
Volume:
Proceedings of the Second International Workshop on Parsing Technologies
Month:
February 13-25
Year:
1991
Address:
Cancun, Mexico
Venues:
IWPT | WS
SIG:
SIGPARSE
Publisher:
Association for Computational Linguistics
Note:
Pages:
193–199
Language:
URL:
https://aclanthology.org/1991.iwpt-1.22
DOI:
Bibkey:
Cite (ACL):
David M. Magerman and Mitchell P. Marcus. 1991. Pearl: A Probabilistic Chart Parser. In Proceedings of the Second International Workshop on Parsing Technologies, pages 193–199, Cancun, Mexico. Association for Computational Linguistics.
Cite (Informal):
Pearl: A Probabilistic Chart Parser (Magerman & Marcus, IWPT 1991)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/1991.iwpt-1.22.pdf