Efficient supervised and semi-supervised approaches for affiliations disambiguation
Identifieur interne : 000198 ( Main/Exploration ); précédent : 000197; suivant : 000199Efficient supervised and semi-supervised approaches for affiliations disambiguation
Auteurs : Pascal Cuxac [France] ; Jean-Charles Lamirel [France] ; Valerie Bonvallot [France]Source :
- Scientometrics : (Print) [ 0138-9130 ] ; 2013.
Descripteurs français
- Pascal (Inist)
- Wicri :
- topic : Classification, Recherche scientifique.
- mix :
English descriptors
- KwdEn :
Abstract
The disambiguation of named entities is a challenge in many fields such as scientometrics, social networks, record linkage, citation analysis, semantic web...etc. The names ambiguities can arise from misspelling, typographical or OCR mistakes, abbreviations, omissions... Therefore, the search of names of persons or of organizations is difficult as soon as a single name might appear in many different forms. This paper proposes two approaches to disambiguate on the affiliations of authors of scientific papers in bibliographic databases: the first way considers that a training dataset is available, and uses a Naive Bayes model. The second way assumes that there is no learning resource, and uses a semi-supervised approach, mixing soft-clustering and Bayesian learning. The results are encouraging and the approach is already partially applied in a scientific survey department. However, our experiments also highlight that our approach has some limitations: it cannot process efficiently highly unbalanced data. Alternatives solutions are possible for future developments, particularly with the use of a recent clustering algorithm relying on feature maximization.
Url:
Affiliations:
- France
- Lorraine
- Nancy, Vandœuvre-lès-Nancy
- Centre national de la recherche scientifique, Laboratoire lorrain de recherche en informatique et ses applications, Synalp (Loria), Université de Lorraine
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Le document en format XML
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research</term><term>Scientometrics</term><term>Semantic analysis</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Domaine recherche</term><term>Scientométrie</term><term>Analyse citation</term><term>Analyse sémantique</term><term>Base de données bibliographiques</term><term>Amas</term><term>Algorithme</term><term>Classification</term><term>Recherche scientifique</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Classification</term><term>Recherche scientifique</term></keywords><keywords scheme="mix" xml:lang="fr"><term>Clustering</term><term>affiliations</term><term>classification automatique</term><term>désambiguisation</term><term>infométrie</term><term>texte</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The disambiguation of named entities is a challenge in many fields such as scientometrics, social networks, record linkage, citation analysis, semantic web...etc. The names ambiguities can arise from misspelling, typographical or OCR mistakes, abbreviations, omissions... Therefore, the search of names of persons or of organizations is difficult as soon as a single name might appear in many different forms. This paper proposes two approaches to disambiguate on the affiliations of authors of scientific papers in bibliographic databases: the first way considers that a training dataset is available, and uses a Naive Bayes model. The second way assumes that there is no learning resource, and uses a semi-supervised approach, mixing soft-clustering and Bayesian learning. The results are encouraging and the approach is already partially applied in a scientific survey department. However, our experiments also highlight that our approach has some limitations: it cannot process efficiently highly unbalanced data. Alternatives solutions are possible for future developments, particularly with the use of a recent clustering algorithm relying on feature maximization.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Lorraine</li></region><settlement><li>Nancy</li><li>Vandœuvre-lès-Nancy</li></settlement><orgName><li>Centre national de la recherche scientifique</li><li>Laboratoire lorrain de recherche en informatique et ses applications</li><li>Synalp (Loria)</li><li>Université de Lorraine</li></orgName></list><tree><country name="France"><noRegion><name sortKey="Cuxac, Pascal" sort="Cuxac, Pascal" uniqKey="Cuxac P" first="Pascal" last="Cuxac">Pascal Cuxac</name></noRegion><name sortKey="Bonvallot, Valerie" sort="Bonvallot, Valerie" uniqKey="Bonvallot V" first="Valerie" last="Bonvallot">Valerie Bonvallot</name><name sortKey="Lamirel, Jean Charles" sort="Lamirel, Jean Charles" uniqKey="Lamirel J" first="Jean-Charles" last="Lamirel">Jean-Charles Lamirel</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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