<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="boullier-2003-supertagging">
    <titleInfo>
        <title>Supertagging: A Non-Statistical Parsing-Based Approach</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Pierre</namePart>
        <namePart type="family">Boullier</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2003-04</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Eighth International Conference on Parsing Technologies</title>
        </titleInfo>
        <originInfo>
            <place>
                <placeTerm type="text">Nancy, France</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We present a novel approach to supertagging w.r.t. some lexicalized grammar G. It differs from previous approaches in several ways:- These supertaggers rely only on structural information: they do not need any training phase;- These supertaggers do not compute the “best“ supertag for each word, but rather a set of supertags. These sets of supertags do not exclude any supertag that will eventually be used in a valid complete derivation (i.e., we have a recall score of 100%);- These supertaggers are in fact true parsers which accept supersets of L(G) that can be more efficiently parsed than the sentences of L(G).</abstract>
    <identifier type="citekey">boullier-2003-supertagging</identifier>
    <location>
        <url>https://aclanthology.org/W03-3006</url>
    </location>
    <part>
        <date>2003-04</date>
        <extent unit="page">
            <start>55</start>
            <end>65</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Supertagging: A Non-Statistical Parsing-Based Approach
%A Boullier, Pierre
%S Proceedings of the Eighth International Conference on Parsing Technologies
%D 2003
%8 April
%C Nancy, France
%F boullier-2003-supertagging
%X We present a novel approach to supertagging w.r.t. some lexicalized grammar G. It differs from previous approaches in several ways:- These supertaggers rely only on structural information: they do not need any training phase;- These supertaggers do not compute the “best“ supertag for each word, but rather a set of supertags. These sets of supertags do not exclude any supertag that will eventually be used in a valid complete derivation (i.e., we have a recall score of 100%);- These supertaggers are in fact true parsers which accept supersets of L(G) that can be more efficiently parsed than the sentences of L(G).
%U https://aclanthology.org/W03-3006
%P 55-65

Markdown (Informal)

[Supertagging: A Non-Statistical Parsing-Based Approach](https://aclanthology.org/W03-3006) (Boullier, IWPT 2003)

• Supertagging: A Non-Statistical Parsing-Based Approach (Boullier, IWPT 2003)

ACL

• Pierre Boullier. 2003. Supertagging: A Non-Statistical Parsing-Based Approach. In Proceedings of the Eighth International Conference on Parsing Technologies, pages 55–65, Nancy, France.