<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lavie-rose-2000-optimal">
    <titleInfo>
        <title>Optimal Ambiguity Packing in Context-free Parsers with Interleaved Unification</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">Carolyn</namePart>
        <namePart type="given">Penstein</namePart>
        <namePart type="family">Rosé</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2000-feb&quot; 23-25&quot;</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Sixth International Workshop on Parsing Technologies</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Trento, Italy</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Ambiguity packing is a well known technique for enhancing the efficiency of context-free parsers. However, in the case of unification-augmented context-free parsers where parsing is interleaved with feature unification, the propagation of feature structures imposes difficulties on the ability of the parser to effectively perform ambiguity packing. We demonstrate that a clever heuristic for prioritizing the execution order of grammar rules and parsing actions can achieve a high level of ambiguity packing that is provably optimal. We present empirical evaluations of the proposed technique, performed with both a Generalized LR parser and a chart parser, that demonstrate its effectiveness.</abstract>
    <identifier type="citekey">lavie-rose-2000-optimal</identifier>
    <location>
        <url>https://aclanthology.org/2000.iwpt-1.16</url>
    </location>
    <part>
        <date>2000-feb&quot; 23-25&quot;</date>
        <extent unit="page">
            <start>147</start>
            <end>158</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Optimal Ambiguity Packing in Context-free Parsers with Interleaved Unification
%A Lavie, Alon
%A Rosé, Carolyn Penstein
%S Proceedings of the Sixth International Workshop on Parsing Technologies
%D 2000
%8 feb" 23 25"
%I Association for Computational Linguistics
%C Trento, Italy
%F lavie-rose-2000-optimal
%X Ambiguity packing is a well known technique for enhancing the efficiency of context-free parsers. However, in the case of unification-augmented context-free parsers where parsing is interleaved with feature unification, the propagation of feature structures imposes difficulties on the ability of the parser to effectively perform ambiguity packing. We demonstrate that a clever heuristic for prioritizing the execution order of grammar rules and parsing actions can achieve a high level of ambiguity packing that is provably optimal. We present empirical evaluations of the proposed technique, performed with both a Generalized LR parser and a chart parser, that demonstrate its effectiveness.
%U https://aclanthology.org/2000.iwpt-1.16
%P 147-158

Markdown (Informal)

[Optimal Ambiguity Packing in Context-free Parsers with Interleaved Unification](https://aclanthology.org/2000.iwpt-1.16) (Lavie & Rosé, IWPT 2000)

• Optimal Ambiguity Packing in Context-free Parsers with Interleaved Unification (Lavie & Rosé, IWPT 2000)

ACL

• Alon Lavie and Carolyn Penstein Rosé. 2000. Optimal Ambiguity Packing in Context-free Parsers with Interleaved Unification. In Proceedings of the Sixth International Workshop on Parsing Technologies, pages 147–158, Trento, Italy. Association for Computational Linguistics.