Automatic annotation extension and classification of documents using a probabilistic graphical model
Identifieur interne : 001141 ( Hal/Curation ); précédent : 001140; suivant : 001142Automatic annotation extension and classification of documents using a probabilistic graphical model
Auteurs : Abdessalem Bouzaieni [France] ; Sabine Barrat [France] ; Salvatore Tabbone [France]Source :
Abstract
With the fast growth of document images, the domain of document annotations has become aresearch area of great interest. Annotations allow to describe the semantic content of documents, and facilitatethe use and research task for the user. However, for a huge number of documents it is a tedious taskto annote each document manually. A solution is to annote a small part of the documents and to extendthis annotation automatically to the whole dataset. In this paper, we propose a model for annotation extensionand for documents classification using a probabilistic graphical model. In this model, we combine visual andtextual characteristics and we show that the integration of the user feedback improves significantly the results.
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DOI: 10.1109/ICDAR.2015.7333775
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<front><div type="abstract" xml:lang="en">With the fast growth of document images, the domain of document annotations has become aresearch area of great interest. Annotations allow to describe the semantic content of documents, and facilitatethe use and research task for the user. However, for a huge number of documents it is a tedious taskto annote each document manually. A solution is to annote a small part of the documents and to extendthis annotation automatically to the whole dataset. In this paper, we propose a model for annotation extensionand for documents classification using a probabilistic graphical model. In this model, we combine visual andtextual characteristics and we show that the integration of the user feedback improves significantly the results.</div>
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<hal api="V3"><titleStmt><title xml:lang="en">Automatic annotation extension and classification of documents using a probabilistic graphical model</title>
<author role="aut"><persName><forename type="first">Abdessalem</forename>
<surname>Bouzaieni</surname>
</persName>
<email></email>
<idno type="halauthor">1271297</idno>
<affiliation ref="#struct-206044"></affiliation>
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<author role="aut"><persName><forename type="first">Sabine</forename>
<surname>Barrat</surname>
</persName>
<email></email>
<idno type="halauthor">979561</idno>
<affiliation ref="#struct-204893"></affiliation>
</author>
<author role="aut"><persName><forename type="first">Salvatore</forename>
<surname>Tabbone</surname>
</persName>
<email>Antoine.Tabbone@loria.fr</email>
<idno type="halauthor">231629</idno>
<affiliation ref="#struct-206044"></affiliation>
</author>
<editor role="depositor"><persName><forename>Salvatore</forename>
<surname>Tabbone</surname>
</persName>
<email>tabbone@loria.fr</email>
</editor>
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<editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2016-01-12 20:01:24</date>
<date type="whenModified">2016-04-28 15:43:54</date>
<date type="whenReleased">2016-01-12 20:01:24</date>
<date type="whenProduced">2015-08-23</date>
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<name key="103151"><persName><forename>Salvatore</forename>
<surname>Tabbone</surname>
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<email>tabbone@loria.fr</email>
</name>
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<idno type="halRefHtml">13th International Conference on Document Analysis and Recognition (ICDAR 2014), Aug 2015, Nancy, France. <10.1109/ICDAR.2015.7333775></idno>
<idno type="halRef">13th International Conference on Document Analysis and Recognition (ICDAR 2014), Aug 2015, Nancy, France. <10.1109/ICDAR.2015.7333775></idno>
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<seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno>
<idno type="stamp" n="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno>
<idno type="stamp" n="LORIA2">Publications du LORIA</idno>
<idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno>
<idno type="stamp" n="UNIV-TOURS">Université François Rabelais</idno>
<idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno>
<idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno>
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<note type="popular" n="0">No</note>
<note type="peer" n="1">Yes</note>
<note type="proceedings" n="1">Yes</note>
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<author role="aut"><persName><forename type="first">Abdessalem</forename>
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<idno type="halAuthorId">979561</idno>
<affiliation ref="#struct-204893"></affiliation>
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<author role="aut"><persName><forename type="first">Salvatore</forename>
<surname>Tabbone</surname>
</persName>
<email>Antoine.Tabbone@loria.fr</email>
<idno type="halAuthorId">231629</idno>
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<monogr><meeting><title>13th International Conference on Document Analysis and Recognition (ICDAR 2014)</title>
<date type="start">2015-08-23</date>
<date type="end">2015-08-26</date>
<settlement>Nancy</settlement>
<country key="FR">France</country>
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<idno type="doi">10.1109/ICDAR.2015.7333775</idno>
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<abstract xml:lang="en">With the fast growth of document images, the domain of document annotations has become aresearch area of great interest. Annotations allow to describe the semantic content of documents, and facilitatethe use and research task for the user. However, for a huge number of documents it is a tedious taskto annote each document manually. A solution is to annote a small part of the documents and to extendthis annotation automatically to the whole dataset. In this paper, we propose a model for annotation extensionand for documents classification using a probabilistic graphical model. In this model, we combine visual andtextual characteristics and we show that the integration of the user feedback improves significantly the results.</abstract>
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