InforLorV4, Hal, Curation, bibRecord, 004A70

Text adaptation using formal concept analysis

Identifieur interne : 004A70 ( Hal/Curation ); précédent : 004A69; suivant : 004A71

Text adaptation using formal concept analysis

Auteurs : Valmi Dufour-Lussier [France] ; Jean Lieber [France] ; Emmanuel Nauer [France] ; Yannick Toussaint [France]

Source :

RBID : Hal:hal-00509030

English descriptors

mix :
- formal concept analysis, natural language processing, text mining, textual case-based reasoning.

Abstract

This paper addresses the issue of adapting cases represented by plain text with the help of formal concept analysis and natural language processing technologies. The actual cases represent recipes in which we classify ingredients according to culinary techniques applied to them. The complex nature of linguistic anaphoras in recipe texts make usual text mining techniques inefficient so a stronger approach, using syntactic and dynamic semantic analysis to build a formal representation of a recipe, had to be used. This representation is useful for various applications but, in this paper, we show how one can extract ingredient–action relations from it in order to use formal concept analysis and select an appropriate replacement sequence of culinary actions to use in adapting the recipe text.

Url:

https://hal.archives-ouvertes.fr/hal-00509030

DOI: 10.1007/978-3-642-14274-1_9

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Hal:hal-00509030

Le document en format XML

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<front><div type="abstract" xml:lang="en">This paper addresses the issue of adapting cases represented by plain text with the help of formal concept analysis and natural language processing technologies. The actual cases represent recipes in which we classify ingredients according to culinary techniques applied to them. The complex nature of linguistic anaphoras in recipe texts make usual text mining techniques inefficient so a stronger approach, using syntactic and dynamic semantic analysis to build a formal representation of a recipe, had to be used. This representation is useful for various applications but, in this paper, we show how one can extract ingredient–action relations from it in order to use formal concept analysis and select an appropriate replacement sequence of culinary actions to use in adapting the recipe text.</div>
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<hal api="V3"><titleStmt><title xml:lang="en">Text adaptation using formal concept analysis</title>
<author role="aut"><persName><forename type="first">Valmi</forename>
<surname>Dufour-Lussier</surname>
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<author role="aut"><persName><forename type="first">Jean</forename>
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<author role="aut"><persName><forename type="first">Emmanuel</forename>
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<author role="aut"><persName><forename type="first">Yannick</forename>
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<editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2010-08-09 21:18:13</date>
<date type="whenWritten">2010-07-27</date>
<date type="whenModified">2016-05-18 08:53:33</date>
<date type="whenReleased">2010-08-10 09:45:37</date>
<date type="whenProduced">2010-07-26</date>
<date type="whenEndEmbargoed">2010-08-09</date>
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<idno type="halRefHtml">Isabelle Bichindaritz and Stefania Montani. 18th International Conference on Case-Based Reasoning - ICCBR 2010, Jul 2010, Alessandria, Italy. Springer-Verlag, 6176, pp.96-110, 2010, Lecture Notes in Artificial Intelligence. <10.1007/978-3-642-14274-1_9></idno>
<idno type="halRef">Isabelle Bichindaritz and Stefania Montani. 18th International Conference on Case-Based Reasoning - ICCBR 2010, Jul 2010, Alessandria, Italy. Springer-Verlag, 6176, pp.96-110, 2010, Lecture Notes in Artificial Intelligence. <10.1007/978-3-642-14274-1_9></idno>
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<idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno>
<idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno>
<idno type="stamp" n="LORIA2">Publications du LORIA</idno>
<idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno>
<idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno>
<idno type="stamp" n="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno>
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<idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">Text adaptation using formal concept analysis</title>
<author role="aut"><persName><forename type="first">Valmi</forename>
<surname>Dufour-Lussier</surname>
</persName>
<email>valmi.dufour@loria.fr</email>
<idno type="halAuthorId">516243</idno>
<affiliation ref="#struct-2358"></affiliation>
</author>
<author role="aut"><persName><forename type="first">Jean</forename>
<surname>Lieber</surname>
</persName>
<email>lieber@loria.fr</email>
<idno type="halAuthorId">129881</idno>
<orgName ref="#struct-364475"></orgName>
<affiliation ref="#struct-2358"></affiliation>
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<author role="aut"><persName><forename type="first">Emmanuel</forename>
<surname>Nauer</surname>
</persName>
<email>Emmanuel.Nauer@loria.fr</email>
<idno type="halAuthorId">66742</idno>
<affiliation ref="#struct-2358"></affiliation>
</author>
<author role="aut"><persName><forename type="first">Yannick</forename>
<surname>Toussaint</surname>
</persName>
<email>Yannick.Toussaint@loria.fr</email>
<idno type="halAuthorId">238979</idno>
<orgName ref="#struct-300009"></orgName>
<affiliation ref="#struct-2358"></affiliation>
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<monogr><title level="m">Case-Based Reasoning Research and Development</title>
<meeting><title>18th International Conference on Case-Based Reasoning - ICCBR 2010</title>
<date type="start">2010-07-26</date>
<date type="end">2010-07-30</date>
<settlement>Alessandria</settlement>
<country key="IT">Italy</country>
</meeting>
<editor>Isabelle Bichindaritz and Stefania Montani</editor>
<imprint><publisher>Springer-Verlag</publisher>
<biblScope unit="serie">Lecture Notes in Artificial Intelligence</biblScope>
<biblScope unit="volume">6176</biblScope>
<biblScope unit="pp">96-110</biblScope>
<date type="datePub">2010-07-27</date>
</imprint>
</monogr>
<idno type="doi">10.1007/978-3-642-14274-1_9</idno>
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<profileDesc><langUsage><language ident="en">English</language>
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<textClass><keywords scheme="author"><term xml:lang="en">formal concept analysis</term>
<term xml:lang="en">natural language processing</term>
<term xml:lang="en">text mining</term>
<term xml:lang="en">textual case-based reasoning</term>
</keywords>
<classCode scheme="halDomain" n="info.info-ai">Computer Science [cs]/Artificial Intelligence [cs.AI]</classCode>
<classCode scheme="halDomain" n="info.info-tt">Computer Science [cs]/Document and Text Processing</classCode>
<classCode scheme="halTypology" n="COMM">Conference papers</classCode>
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<abstract xml:lang="en">This paper addresses the issue of adapting cases represented by plain text with the help of formal concept analysis and natural language processing technologies. The actual cases represent recipes in which we classify ingredients according to culinary techniques applied to them. The complex nature of linguistic anaphoras in recipe texts make usual text mining techniques inefficient so a stronger approach, using syntactic and dynamic semantic analysis to build a formal representation of a recipe, had to be used. This representation is useful for various applications but, in this paper, we show how one can extract ingredient–action relations from it in order to use formal concept analysis and select an appropriate replacement sequence of culinary actions to use in adapting the recipe text.</abstract>
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Text adaptation using formal concept analysis

Text adaptation using formal concept analysis

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