OcrV1, France, Analysis, bibRecord, 000069

Efficient supervised and semi-supervised approaches for affliations disambiguation

Identifieur interne : 000069 ( France/Analysis ); précédent : 000068; suivant : 000070

Efficient supervised and semi-supervised approaches for affliations disambiguation

Auteurs : Pascal Cuxac [France] ; Valérie Bonvallot [France] ; Jean-Charles Lamirel [France]

Source :

RBID : Hal:hal-00956386

Descripteurs français

mix :
- Clustering, classification automatique, infométrie, texte.

Abstract

The disambiguation of named entities is a challenge in many elds such as sciento- metrics, social networks, record linkage, citation analysis, semantic web...etc. The names ambiguities can arise from misspelling, typographical or OCR mistakes, abbreviations, omissions... So the search of names of persons or of organization is di cult, a single name can appear in di erent forms. This paper proposes two approaches to disambiguate on the a liations of authors of sci- enti c papers in bibliographic databases: the rst way, considers that we have a training corpus, and uses a Naive Bayesian model. The second way assumes that we have not re- source learning, and uses a semi-supervised approach, mixing soft-clustering and Bayesian learning. The results are encouraging and are already partially applied in a scienti c survey department. However, we aware that our approach may have limitations: we can't process e ciently highly unbalanced data but solutions are possible for future developments.

Url:

https://hal.archives-ouvertes.fr/hal-00956386

Affiliations:

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

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to stream Main, to step Curation: 000222
to stream Main, to step Exploration: 000222
to stream France, to step Extraction: 000069

Links to Exploration step

Hal:hal-00956386

Le document en format XML

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<front><div type="abstract" xml:lang="en">The disambiguation of named entities is a challenge in many elds such as sciento- metrics, social networks, record linkage, citation analysis, semantic web...etc. The names ambiguities can arise from misspelling, typographical or OCR mistakes, abbreviations, omissions... So the search of names of persons or of organization is di cult, a single name can appear in di erent forms. This paper proposes two approaches to disambiguate on the a liations of authors of sci- enti c papers in bibliographic databases: the rst way, considers that we have a training corpus, and uses a Naive Bayesian model. The second way assumes that we have not re- source learning, and uses a semi-supervised approach, mixing soft-clustering and Bayesian learning. The results are encouraging and are already partially applied in a scienti c survey department. However, we aware that our approach may have limitations: we can't process e ciently highly unbalanced data but solutions are possible for future developments.</div>
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Pour manipuler ce document sous Unix (Dilib)

EXPLOR_STEP=$WICRI_ROOT/Ticri/CIDE/explor/OcrV1/Data/France/Analysis

HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000069 | SxmlIndent | more

HfdSelect -h $EXPLOR_AREA/Data/France/Analysis/biblio.hfd -nk 000069 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Ticri/CIDE
   |area=    OcrV1
   |flux=    France
   |étape=   Analysis
   |type=    RBID
   |clé=     Hal:hal-00956386
   |texte=   Efficient supervised and semi-supervised approaches for affliations disambiguation
}}

This area was generated with Dilib version V0.6.32.
Data generation: Sat Nov 11 16:53:45 2017. Site generation: Mon Mar 11 23:15:16 2024

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Serveur d'exploration sur l'OCR

Efficient supervised and semi-supervised approaches for affliations disambiguation

Efficient supervised and semi-supervised approaches for affliations disambiguation

Source :

Descripteurs français

Abstract

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

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri