Quality Flaws Prediction in Wikipedia by Using Deep Learning Approaches
- Autores
- Capodici, Gianfranco; Bazán Pereyra, Gerónimo; Bonnin, Rodolfo; Ferretti, Edgardo
- Año de publicación
- 2022
- Idioma
- español castellano
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Quality flaws prediction in Wikipedia is an ongoing research trend. In particular, in this work we tackle the problem of automatically predicting four out of the ten most frequent quality flaws; namely: No footnotes, Notability, Primary Sources and Refmprove. Different deep learning state-of-the-art approaches were evaluated on the test corpus from the 1st International Competition on Quality Flaw Prediction in Wikipedia; a well-known uniform evaluation corpus from this research field. Particularly, the results show that TabNet reachs or improves the existing benchmarks for the Notability and Refmprove flaws, and performs in a very competitive way for the other two remaining flaws.
XIX Workshop base de datos y Minería de datos (WBDMD)
Red de Universidades con Carreras en Informática - Materia
-
Ciencias Informáticas
Wikipedia
Information Quality
Quality Flaws Prediction
Deep Learning - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/149435
Ver los metadatos del registro completo
id |
SEDICI_5a8250da35dcda9e7ea46faa36aaef72 |
---|---|
oai_identifier_str |
oai:sedici.unlp.edu.ar:10915/149435 |
network_acronym_str |
SEDICI |
repository_id_str |
1329 |
network_name_str |
SEDICI (UNLP) |
spelling |
Quality Flaws Prediction in Wikipedia by Using Deep Learning ApproachesCapodici, GianfrancoBazán Pereyra, GerónimoBonnin, RodolfoFerretti, EdgardoCiencias InformáticasWikipediaInformation QualityQuality Flaws PredictionDeep LearningQuality flaws prediction in Wikipedia is an ongoing research trend. In particular, in this work we tackle the problem of automatically predicting four out of the ten most frequent quality flaws; namely: No footnotes, Notability, Primary Sources and Refmprove. Different deep learning state-of-the-art approaches were evaluated on the test corpus from the 1st International Competition on Quality Flaw Prediction in Wikipedia; a well-known uniform evaluation corpus from this research field. Particularly, the results show that TabNet reachs or improves the existing benchmarks for the Notability and Refmprove flaws, and performs in a very competitive way for the other two remaining flaws.XIX Workshop base de datos y Minería de datos (WBDMD)Red de Universidades con Carreras en Informática2022-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf375-384http://sedici.unlp.edu.ar/handle/10915/149435spainfo:eu-repo/semantics/altIdentifier/isbn/978-987-1364-31-2info:eu-repo/semantics/reference/hdl/10915/149102info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:38:21Zoai:sedici.unlp.edu.ar:10915/149435Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:38:22.051SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Quality Flaws Prediction in Wikipedia by Using Deep Learning Approaches |
title |
Quality Flaws Prediction in Wikipedia by Using Deep Learning Approaches |
spellingShingle |
Quality Flaws Prediction in Wikipedia by Using Deep Learning Approaches Capodici, Gianfranco Ciencias Informáticas Wikipedia Information Quality Quality Flaws Prediction Deep Learning |
title_short |
Quality Flaws Prediction in Wikipedia by Using Deep Learning Approaches |
title_full |
Quality Flaws Prediction in Wikipedia by Using Deep Learning Approaches |
title_fullStr |
Quality Flaws Prediction in Wikipedia by Using Deep Learning Approaches |
title_full_unstemmed |
Quality Flaws Prediction in Wikipedia by Using Deep Learning Approaches |
title_sort |
Quality Flaws Prediction in Wikipedia by Using Deep Learning Approaches |
dc.creator.none.fl_str_mv |
Capodici, Gianfranco Bazán Pereyra, Gerónimo Bonnin, Rodolfo Ferretti, Edgardo |
author |
Capodici, Gianfranco |
author_facet |
Capodici, Gianfranco Bazán Pereyra, Gerónimo Bonnin, Rodolfo Ferretti, Edgardo |
author_role |
author |
author2 |
Bazán Pereyra, Gerónimo Bonnin, Rodolfo Ferretti, Edgardo |
author2_role |
author author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Wikipedia Information Quality Quality Flaws Prediction Deep Learning |
topic |
Ciencias Informáticas Wikipedia Information Quality Quality Flaws Prediction Deep Learning |
dc.description.none.fl_txt_mv |
Quality flaws prediction in Wikipedia is an ongoing research trend. In particular, in this work we tackle the problem of automatically predicting four out of the ten most frequent quality flaws; namely: No footnotes, Notability, Primary Sources and Refmprove. Different deep learning state-of-the-art approaches were evaluated on the test corpus from the 1st International Competition on Quality Flaw Prediction in Wikipedia; a well-known uniform evaluation corpus from this research field. Particularly, the results show that TabNet reachs or improves the existing benchmarks for the Notability and Refmprove flaws, and performs in a very competitive way for the other two remaining flaws. XIX Workshop base de datos y Minería de datos (WBDMD) Red de Universidades con Carreras en Informática |
description |
Quality flaws prediction in Wikipedia is an ongoing research trend. In particular, in this work we tackle the problem of automatically predicting four out of the ten most frequent quality flaws; namely: No footnotes, Notability, Primary Sources and Refmprove. Different deep learning state-of-the-art approaches were evaluated on the test corpus from the 1st International Competition on Quality Flaw Prediction in Wikipedia; a well-known uniform evaluation corpus from this research field. Particularly, the results show that TabNet reachs or improves the existing benchmarks for the Notability and Refmprove flaws, and performs in a very competitive way for the other two remaining flaws. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-10 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/149435 |
url |
http://sedici.unlp.edu.ar/handle/10915/149435 |
dc.language.none.fl_str_mv |
spa |
language |
spa |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/isbn/978-987-1364-31-2 info:eu-repo/semantics/reference/hdl/10915/149102 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
dc.format.none.fl_str_mv |
application/pdf 375-384 |
dc.source.none.fl_str_mv |
reponame:SEDICI (UNLP) instname:Universidad Nacional de La Plata instacron:UNLP |
reponame_str |
SEDICI (UNLP) |
collection |
SEDICI (UNLP) |
instname_str |
Universidad Nacional de La Plata |
instacron_str |
UNLP |
institution |
UNLP |
repository.name.fl_str_mv |
SEDICI (UNLP) - Universidad Nacional de La Plata |
repository.mail.fl_str_mv |
alira@sedici.unlp.edu.ar |
_version_ |
1844616258708832256 |
score |
13.070432 |