Bibliography References Validation using Emergent Architecture
Identifieur interne : 00CB61 ( Main/Exploration ); précédent : 00CB60; suivant : 00CB62Bibliography References Validation using Emergent Architecture
Auteurs : F. Parmentier ; Abdel Belaïd [France]Source :
English descriptors
- KwdEn :
Abstract
In this paper, we present an AI approach for the semantic recognition of bibliography references. The objectif is to produce for each reference a structured flow containing, in UNIMARG format, the list of the different sub-fields recognized and semantically validated. References are given by an OCR flow in SGML format. For the component recognition, we use a generic model of a reference class and an approach based on hypotheses production and verification about component limits in the reference area. Once limits are found, each component is validated according to the relationships between its terms (for example, co-authors) and between terms within different components (for example, relationships between authors and keywords (themes) in the title). These relationships are validated from references written by the same authors or by authors in close domains and located in a reference data base. We use an emergent architecture based on a Concept Network. The Concept Network is dynamic\, ; it allows concepts and, consequently, ``agents'' to emerge. This new architecture is experimented on some paper references belonging to our laboratory references.
Affiliations:
- France
- Grand Est, Lorraine (région)
- Nancy
- Centre national de la recherche scientifique, Institut national de recherche en informatique et en automatique, Laboratoire lorrain de recherche en informatique et ses applications, Université de Lorraine
Links toward previous steps (curation, corpus...)
- to stream Crin, to step Corpus: 001624
- to stream Crin, to step Curation: 001624
- to stream Crin, to step Checkpoint: 003130
- to stream Main, to step Merge: 00D428
- to stream Main, to step Curation: 00CB61
Le document en format XML
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<front><div type="abstract" xml:lang="en" wicri:score="3013">In this paper, we present an AI approach for the semantic recognition of bibliography references. The objectif is to produce for each reference a structured flow containing, in UNIMARG format, the list of the different sub-fields recognized and semantically validated. References are given by an OCR flow in SGML format. For the component recognition, we use a generic model of a reference class and an approach based on hypotheses production and verification about component limits in the reference area. Once limits are found, each component is validated according to the relationships between its terms (for example, co-authors) and between terms within different components (for example, relationships between authors and keywords (themes) in the title). These relationships are validated from references written by the same authors or by authors in close domains and located in a reference data base. We use an emergent architecture based on a Concept Network. The Concept Network is dynamic\, ; it allows concepts and, consequently, ``agents'' to emerge. This new architecture is experimented on some paper references belonging to our laboratory references.</div>
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