@inproceedings{lavie-tomita-1993-glr,
title = "{GLR}* {--} An Efficient Noise-skipping Parsing Algorithm For Context Free Grammars",
author = "Lavie, Alon and
Tomita, Masaru",
editor = "Bunt, Harry and
Berwick, Robert and
Church, Ken and
Joshi, Aravind and
Kaplan, Ronald and
Kay, Martin and
Lang, Bernard and
Nagao, Makoto and
Nijholt, Anton and
Steedman, Mark and
Thompson, Henry and
Tomita, Masaru and
Vijay-Shanker, K. and
Wilks, Yorick and
Wittenburg, Kent",
booktitle = "Proceedings of the Third International Workshop on Parsing Technologies",
month = aug # " 10-13",
year = "1993",
address = "Tilburg, Netherlands and Durbuy, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/1993.iwpt-1.12",
pages = "123--134",
abstract = "This paper describes GLR*, a parser that can parse any input sentence by ignoring unrecognizable parts of the sentence. In case the standard parsing procedure fails to parse an input sentence, the parser nondeterministically skips some word(s) in the sentence, and returns the parse with fewest skipped words. Therefore, the parser will return some parse(s) with any input sentence, unless no part of the sentence can be recognized at all. The problem can be defined in the following way: Given a context-free grammar $G$ and a sentence $S$, find and parse $S'$ {--} the largest subset of words of $S$, such that $S' \in L(G)$. The algorithm described in this paper is a modification of the Generalized LR (Tomita) parsing algorithm [Tomita, 1986] . The parser accommodates the skipping of words by allowing shift operations to be performed from inactive state nodes of the Graph Structured Stack. A heuristic similar to beam search makes the algorithm computationally tractable. There have been several other approaches to the problem of robust parsing, most of which are special purpose algorithms [Carbonell and Hayes, 1984] , [Ward, 1991] and others. Because our approach is a modification to a standard context-free parsing algorithm, all the techniques and grammars developed for the standard parser can be applied as they are. Also, in case the input sentence is by itself grammatical, our parser behaves exactly as the standard GLR parser. The modified parser, GLR*, has been implemented and integrated with the latest version of the Generalized LR Parser/Compiler [Tomita et al , 1988], [Tomita, 1990]. We discuss an application of the GLR* parser to spontaneous speech understanding and present some preliminary tests on the utility of the GLR* parser in such settings.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lavie-tomita-1993-glr">
<titleInfo>
<title>GLR* – An Efficient Noise-skipping Parsing Algorithm For Context Free Grammars</title>
</titleInfo>
<name type="personal">
<namePart type="given">Alon</namePart>
<namePart type="family">Lavie</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Masaru</namePart>
<namePart type="family">Tomita</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>1993-aug 10-13</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Third International Workshop on Parsing Technologies</title>
</titleInfo>
<name type="personal">
<namePart type="given">Harry</namePart>
<namePart type="family">Bunt</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">Ken</namePart>
<namePart type="family">Church</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">Martin</namePart>
<namePart type="family">Kay</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bernard</namePart>
<namePart type="family">Lang</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">Anton</namePart>
<namePart type="family">Nijholt</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mark</namePart>
<namePart type="family">Steedman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Henry</namePart>
<namePart type="family">Thompson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<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">K</namePart>
<namePart type="family">Vijay-Shanker</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>
<name type="personal">
<namePart type="given">Kent</namePart>
<namePart type="family">Wittenburg</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Tilburg, Netherlands and Durbuy, Belgium</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper describes GLR*, a parser that can parse any input sentence by ignoring unrecognizable parts of the sentence. In case the standard parsing procedure fails to parse an input sentence, the parser nondeterministically skips some word(s) in the sentence, and returns the parse with fewest skipped words. Therefore, the parser will return some parse(s) with any input sentence, unless no part of the sentence can be recognized at all. The problem can be defined in the following way: Given a context-free grammar G and a sentence S, find and parse S’ – the largest subset of words of S, such that S’ ın L(G). The algorithm described in this paper is a modification of the Generalized LR (Tomita) parsing algorithm [Tomita, 1986] . The parser accommodates the skipping of words by allowing shift operations to be performed from inactive state nodes of the Graph Structured Stack. A heuristic similar to beam search makes the algorithm computationally tractable. There have been several other approaches to the problem of robust parsing, most of which are special purpose algorithms [Carbonell and Hayes, 1984] , [Ward, 1991] and others. Because our approach is a modification to a standard context-free parsing algorithm, all the techniques and grammars developed for the standard parser can be applied as they are. Also, in case the input sentence is by itself grammatical, our parser behaves exactly as the standard GLR parser. The modified parser, GLR*, has been implemented and integrated with the latest version of the Generalized LR Parser/Compiler [Tomita et al , 1988], [Tomita, 1990]. We discuss an application of the GLR* parser to spontaneous speech understanding and present some preliminary tests on the utility of the GLR* parser in such settings.</abstract>
<identifier type="citekey">lavie-tomita-1993-glr</identifier>
<location>
<url>https://aclanthology.org/1993.iwpt-1.12</url>
</location>
<part>
<date>1993-aug 10-13</date>
<extent unit="page">
<start>123</start>
<end>134</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T GLR* – An Efficient Noise-skipping Parsing Algorithm For Context Free Grammars
%A Lavie, Alon
%A Tomita, Masaru
%Y Bunt, Harry
%Y Berwick, Robert
%Y Church, Ken
%Y Joshi, Aravind
%Y Kaplan, Ronald
%Y Kay, Martin
%Y Lang, Bernard
%Y Nagao, Makoto
%Y Nijholt, Anton
%Y Steedman, Mark
%Y Thompson, Henry
%Y Tomita, Masaru
%Y Vijay-Shanker, K.
%Y Wilks, Yorick
%Y Wittenburg, Kent
%S Proceedings of the Third International Workshop on Parsing Technologies
%D 1993
%8 aug 10 13
%I Association for Computational Linguistics
%C Tilburg, Netherlands and Durbuy, Belgium
%F lavie-tomita-1993-glr
%X This paper describes GLR*, a parser that can parse any input sentence by ignoring unrecognizable parts of the sentence. In case the standard parsing procedure fails to parse an input sentence, the parser nondeterministically skips some word(s) in the sentence, and returns the parse with fewest skipped words. Therefore, the parser will return some parse(s) with any input sentence, unless no part of the sentence can be recognized at all. The problem can be defined in the following way: Given a context-free grammar G and a sentence S, find and parse S’ – the largest subset of words of S, such that S’ ın L(G). The algorithm described in this paper is a modification of the Generalized LR (Tomita) parsing algorithm [Tomita, 1986] . The parser accommodates the skipping of words by allowing shift operations to be performed from inactive state nodes of the Graph Structured Stack. A heuristic similar to beam search makes the algorithm computationally tractable. There have been several other approaches to the problem of robust parsing, most of which are special purpose algorithms [Carbonell and Hayes, 1984] , [Ward, 1991] and others. Because our approach is a modification to a standard context-free parsing algorithm, all the techniques and grammars developed for the standard parser can be applied as they are. Also, in case the input sentence is by itself grammatical, our parser behaves exactly as the standard GLR parser. The modified parser, GLR*, has been implemented and integrated with the latest version of the Generalized LR Parser/Compiler [Tomita et al , 1988], [Tomita, 1990]. We discuss an application of the GLR* parser to spontaneous speech understanding and present some preliminary tests on the utility of the GLR* parser in such settings.
%U https://aclanthology.org/1993.iwpt-1.12
%P 123-134
Markdown (Informal)
[GLR* – An Efficient Noise-skipping Parsing Algorithm For Context Free Grammars](https://aclanthology.org/1993.iwpt-1.12) (Lavie & Tomita, IWPT 1993)
ACL