InforLorV4, Hal, Checkpoint, bibRecord, 000358

Semantic Label and Structure Model based Approach for Entity Recognition in Database Context

Identifieur interne : 000358 ( Hal/Checkpoint ); précédent : 000357; suivant : 000359

Semantic Label and Structure Model based Approach for Entity Recognition in Database Context

Auteurs : Nihel Kooli [France] ; Abdel Belaïd [France]

Source :

RBID : Hal:hal-01191425

Abstract

—This paper proposes an entity recognition approach in scanned documents referring to their description in database records. First, using the database record values, the corresponding document fields are labeled. Second, entities are identified by their labels and ranked using a TF/IDF based score. For each entity, local labels are grouped into a graph. This graph is matched with a graph model (structure model) which represents geometric structures of local entity labels using a specific cost function. This model is trained on a set of well chosen entities semi-automatically annotated. At the end, a correction step allows us to complete the eventual entity mislabeling using geometrical relationships between labels. The evaluation on 200 business documents containing 500 entities reaches about 93% for recall and 97% for precision.

Url:

https://hal.archives-ouvertes.fr/hal-01191425

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 004484
to stream Hal, to step Curation: 004484

Links to Exploration step

Hal:hal-01191425

Le document en format XML

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<front><div type="abstract" xml:lang="en">—This paper proposes an entity recognition approach in scanned documents referring to their description in database records. First, using the database record values, the corresponding document fields are labeled. Second, entities are identified by their labels and ranked using a TF/IDF based score. For each entity, local labels are grouped into a graph. This graph is matched with a graph model (structure model) which represents geometric structures of local entity labels using a specific cost function. This model is trained on a set of well chosen entities semi-automatically annotated. At the end, a correction step allows us to complete the eventual entity mislabeling using geometrical relationships between labels. The evaluation on 200 business documents containing 500 entities reaches about 93% for recall and 97% for precision.</div>
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<idno type="halAuthorId">1205822</idno>
<affiliation ref="#struct-206042"></affiliation>
</author>
<author role="aut"><persName><forename type="first">Abdel</forename>
<surname>Belaïd</surname>
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<idno type="halAuthorId">52807</idno>
<affiliation ref="#struct-206042"></affiliation>
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<monogr><meeting><title>13th International Conference on Document Analysis and Recognition (ICDAR 2015)</title>
<date type="start">2015-08-24</date>
<date type="end">2015-08-26</date>
<settlement>Nancy</settlement>
<country key="FR">France</country>
</meeting>
<imprint><biblScope unit="pp">5</biblScope>
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<profileDesc><langUsage><language ident="en">English</language>
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<textClass><classCode scheme="halDomain" n="info">Computer Science [cs]</classCode>
<classCode scheme="halDomain" n="info.info-ir">Computer Science [cs]/Information Retrieval [cs.IR]</classCode>
<classCode scheme="halTypology" n="COMM">Conference papers</classCode>
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<abstract xml:lang="en">—This paper proposes an entity recognition approach in scanned documents referring to their description in database records. First, using the database record values, the corresponding document fields are labeled. Second, entities are identified by their labels and ranked using a TF/IDF based score. For each entity, local labels are grouped into a graph. This graph is matched with a graph model (structure model) which represents geometric structures of local entity labels using a specific cost function. This model is trained on a set of well chosen entities semi-automatically annotated. At the end, a correction step allows us to complete the eventual entity mislabeling using geometrical relationships between labels. The evaluation on 200 business documents containing 500 entities reaches about 93% for recall and 97% for precision.</abstract>
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Semantic Label and Structure Model based Approach for Entity Recognition in Database Context

Semantic Label and Structure Model based Approach for Entity Recognition in Database Context

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