In this paper, we present tools for addressing noisy keyword issues in digital libraries. Two tasks, language detection and misspelling detection and correction, are addressed using both machine learning and deep learning techniques. To train and validate the models, different datasets were used/created/scraped. Encouraging preliminary results are presented and discussed.

Machine learning and neural networks tools to address noisy data issues

MT Artese;I Gagliardi
2021

Abstract

In this paper, we present tools for addressing noisy keyword issues in digital libraries. Two tasks, language detection and misspelling detection and correction, are addressed using both machine learning and deep learning techniques. To train and validate the models, different datasets were used/created/scraped. Encouraging preliminary results are presented and discussed.
2021
Istituto di Matematica Applicata e Tecnologie Informatiche - IMATI -
Inglese
Desislava Paneva-Marinova, Radoslav Pavlov, Peter Stanchev, Detelin Luchev
Digital Presentation and Preservation of Cultural and Scientific Heritage
Digital Presentation and Preservation of Cultural and Scientific Heritage
11
89
98
https://dipp.math.bas.bg/dipp/article/view/dipp.2021.11.8
Sì, ma tipo non specificato
27/09/22021
Burgas, Bulgaria
Content based retrieval
Digital library
Noisy data
Tags
Unsupervised tools
Published: 2021-09-10
2
restricted
Artese, Mt; Gagliardi, I
273
info:eu-repo/semantics/conferenceObject
04 Contributo in convegno::04.01 Contributo in Atti di convegno
File in questo prodotto:
File Dimensione Formato  
prod_473982-doc_194009.pdf

solo utenti autorizzati

Descrizione: Machine Learning and Neural Networks Tools to Address Noisy Data Issues
Tipologia: Versione Editoriale (PDF)
Dimensione 321.54 kB
Formato Adobe PDF
321.54 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/417755
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact