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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 3, 2023.
Abstract: In recent years, there is growing interest in intelligent conversation systems. In this context, Question Classification is an essential subtask in Question Answering systems that determines the question type, therefore, also the type of the answer. However, while there is abundant research for English, little research work has been carried out for other languages. In this paper we deal with classification of questions in the Albanian language which is considered a complex Indo-European language. We employ both machine learning and deep learning approaches on a large corpus in Albanian based on the six-class TREC dataset with approximately 5000 questions. Experiments with and without stop-words show that the impact of stop-words is significant in the accuracy of the classifier. Extensive comparison of algorithms for the task of question classification in Albanian show that deep learning algorithms outperform conventional machine learning approaches. To the best of our knowledge this is the first approach in literature for classifying questions in Albanian and the results are highly comparable to English.
Evis Trandafili, Nelda Kote and Gjergj Plepi, “Question Classification in Albanian Through Deep Learning Approaches” International Journal of Advanced Computer Science and Applications(IJACSA), 14(3), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140385
@article{Trandafili2023,
title = {Question Classification in Albanian Through Deep Learning Approaches},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140385},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140385},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {3},
author = {Evis Trandafili and Nelda Kote and Gjergj Plepi}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.