Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloBest practices of convolutional neural networks for question classification
Anno di pubblicazione2020
Formato-
Autore/iPota M.; Esposito M.; De Pietro G.; Fujita H.
Affiliazioni autoriInstitute for High Performance Computing and Networking-National Research Council of Italy (ICAR-CNR), 80131, Naples, Italy; Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), 720000, Ho Chi Minh City, Vietnam; Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18010, Granada, Spain; Faculty of Software and Information Science, Iwate Prefectural University, 020-0693, Iwate, Japan.
Autori CNR e affiliazioni
  • GIUSEPPE DE PIETRO
  • MASSIMO ESPOSITO
  • MARCO POTA
Lingua/e
  • inglese
AbstractQuestion Classification (QC) is of primary importance in question answering systems, since it enables extraction of the correct answer type. State-of-the-art solutions for short text classification obtained remarkable results by Convolutional Neural Networks (CNNs). However, implementing such models requires choices, usually based on subjective experience, or on rare works comparing different settings for general text classification, while peculiar solutions should be individuated for QC task, depending on language and on dataset size. Therefore, this work aims at suggesting best practices for QC using CNNs. Different datasets were employed: (i) A multilingual set of labelled questions to evaluate the dependence of optimal settings on language; (ii) a large, widely used dataset for validation and comparison. Numerous experiments were executed, to perform a multivariate analysis, for evaluating statistical significance and influence on QC performance of all the factors (regarding text representation, architectural characteristics, and learning hyperparameters) and some of their interactions, and for finding the most appropriate strategies for QC. Results show the influence of CNN settings on performance. Optimal settings were found depending on language. Tests on different data validated the optimization performed, and confirmed the transferability of the best settings. Comparisons to configurations suggested by previous works highlight the best classification accuracy by those optimized here. These findings can suggest the best choices to configure a CNN for QC.
Lingua abstractinglese
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RivistaApplied sciences
Attiva dal 2010
Editore: Molecular Diversity Preservation International - Basel
Lingua: inglese
ISSN: 2076-3417
Titolo chiave: Applied sciences
Titolo proprio: Applied sciences.
Titolo abbreviato: Appl. sci.
Numero volume della rivista10
Fascicolo della rivista-
DOI10.3390/app10144710
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-85088660019)
Parole chiavequestion classification, multilingual, convolutional neural networks, Natural Language Processing (NLP), deep learning
Link (URL, URI)http://www.scopus.com/record/display.url?eid=2-s2.0-85088660019&origin=inward
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  • ICAR — Istituto di calcolo e reti ad alte prestazioni
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