@inproceedings{magerman-marcus-1991-pearl,
title = "{P}earl: A Probabilistic Chart Parser",
author = "Magerman, David M. and
Marcus, Mitchell P.",
editor = "Tomita, Masaru and
Kay, Martin and
Berwick, Robert and
Hajicova, Eva and
Joshi, Aravind and
Kaplan, Ronald and
Nagao, Makoto and
Wilks, Yorick",
booktitle = "Proceedings of the Second International Workshop on Parsing Technologies",
month = feb # " 13-25",
year = "1991",
address = "Cancun, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/1991.iwpt-1.22",
pages = "193--199",
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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="magerman-marcus-1991-pearl">
<titleInfo>
<title>Pearl: A Probabilistic Chart Parser</title>
</titleInfo>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="given">M</namePart>
<namePart type="family">Magerman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mitchell</namePart>
<namePart type="given">P</namePart>
<namePart type="family">Marcus</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>1991-feb 13-25</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Second International Workshop on Parsing Technologies</title>
</titleInfo>
<name type="personal">
<namePart type="given">Masaru</namePart>
<namePart type="family">Tomita</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Martin</namePart>
<namePart type="family">Kay</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Robert</namePart>
<namePart type="family">Berwick</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Eva</namePart>
<namePart type="family">Hajicova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aravind</namePart>
<namePart type="family">Joshi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ronald</namePart>
<namePart type="family">Kaplan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Makoto</namePart>
<namePart type="family">Nagao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yorick</namePart>
<namePart type="family">Wilks</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Cancun, Mexico</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">magerman-marcus-1991-pearl</identifier>
<location>
<url>https://aclanthology.org/1991.iwpt-1.22</url>
</location>
<part>
<date>1991-feb 13-25</date>
<extent unit="page">
<start>193</start>
<end>199</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Pearl: A Probabilistic Chart Parser
%A Magerman, David M.
%A Marcus, Mitchell P.
%Y Tomita, Masaru
%Y Kay, Martin
%Y Berwick, Robert
%Y Hajicova, Eva
%Y Joshi, Aravind
%Y Kaplan, Ronald
%Y Nagao, Makoto
%Y Wilks, Yorick
%S Proceedings of the Second International Workshop on Parsing Technologies
%D 1991
%8 feb 13 25
%I Association for Computational Linguistics
%C Cancun, Mexico
%F magerman-marcus-1991-pearl
%X 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.
%U https://aclanthology.org/1991.iwpt-1.22
%P 193-199
Markdown (Informal)
[Pearl: A Probabilistic Chart Parser](https://aclanthology.org/1991.iwpt-1.22) (Magerman & Marcus, IWPT 1991)
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.