Quality Flaws Prediction in Wikipedia by Using Deep Learning Approaches

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...

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Autores principales: Capodici, Gianfranco, Bazán Pereyra, Gerónimo, Bonnin, Rodolfo, Ferretti, Edgardo
Formato: Objeto de conferencia
Lenguaje:Español
Publicado: 2022
Materias:
Ciencias Informáticas
Wikipedia
Information Quality
Quality Flaws Prediction
Deep Learning
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/149435
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-149435
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Español
topic Ciencias Informáticas
Wikipedia
Information Quality
Quality Flaws Prediction
Deep Learning
spellingShingle Ciencias Informáticas
Wikipedia
Information Quality
Quality Flaws Prediction
Deep Learning
Capodici, Gianfranco
Bazán Pereyra, Gerónimo
Bonnin, Rodolfo
Ferretti, Edgardo
Quality Flaws Prediction in Wikipedia by Using Deep Learning Approaches
topic_facet Ciencias Informáticas
Wikipedia
Information Quality
Quality Flaws Prediction
Deep Learning
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.
format Objeto de conferencia
Objeto de conferencia
author Capodici, Gianfranco
Bazán Pereyra, Gerónimo
Bonnin, Rodolfo
Ferretti, Edgardo
author_facet Capodici, Gianfranco
Bazán Pereyra, Gerónimo
Bonnin, Rodolfo
Ferretti, Edgardo
author_sort Capodici, Gianfranco
title Quality Flaws Prediction in Wikipedia by Using Deep Learning Approaches
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
publishDate 2022
url http://sedici.unlp.edu.ar/handle/10915/149435
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AT ferrettiedgardo qualityflawspredictioninwikipediabyusingdeeplearningapproaches
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