| References: [1] Al-Anzi, F.S., AbuZeina, D. (2018). Beyond vector space model for hierarchical arabic text classification: A markov chain approach. Information Processing & Management, 54(1), 105–115.
[2] Alami, N., Meknassi, M., Ouatik, S.A., Ennahnahi, N. (2016). Impact of stemming on arabic text summarization. In: 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt). p 338–343. IEEE.
[3] Bahassine, S., Madani, A., Al-Sarem, M., Kissi, M. (2018). Feature selection using an improved chi-square for arabic text classification. Journal of King Saud University- Computer and Information Sciences.
[4] Dhar, A., Dash, N.S., Roy, K. (2018). Application of tf-idf feature for categorizing documents of online bangla web text corpus. In: Intelligent Engineering Informatics, p .51–59. Springer.
[5] Dhar, A., Dash, N.S., Roy, K. (2018). Categorization of bangla web text documents based on tf-idf-icf text analysis scheme.
In: Annual Convention of the Computer Society of India. p 477–484. Springer.
[6] El Bazzi, M.S., Mammass, D., Ennaji, A., Zaki, T. (2018). Toward a complex system for context discovery to index arabic documents. JCP, 13(8), 955–962.
[7] Ganiz, M.C., Tutkan, M., Akyokuº, S. (2015). A novel classifier based on meaning for text classification. In: 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA). p 1–5. IEEE (2015)
[8] Goncalves, C.A., Iglesias, E.L., Borrajo, L., Camacho, R., Vieira, A.S., Goncalves, C.T. (2019). Comparative study of feature selection methods for medical full text classification In: International Work-Conference on Bioinformatics and Biomedical Engineering. p 550–560. Springer.
[9] Hamza, A., En-Nahnahi, N., Zidani, K. A., Ouatik, S. E. A. (2019). An arabic question classification method based on new taxonomy and continuous distributed representation of words. Journal of King Saud University-Computer and Information Sciences.
[10] Herskovic, J. R., Cohen, T., Subramanian, D., Iyengar, M. S., Smith, J.W., Bernstam, E.V. (2011). Medrank: Using graph based concept ranking to index biomedical texts. International journal of medical informatics, 80 (6), 431–441.
[11] Kumar, B.S., Ravi, V. (2017). Text document classification with pca and one-class svm. In: Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications. p 107–115. Springer.
[12] Labani, M., Moradi, P., Ahmadizar, F., Jalili, M. (2018). A novel multivariate filter method for feature selection in text classification problems. Engineering Applications of Artificial Intelligence, 70, 25–37.
[13] Mohamed, R.,Watada, J. (2010). An evidential reasoning based lsa approach to document classification for knowledge acquisition. In: 2010 IEEE International Conference on Industrial Engineering and Engineering Management. p 1092– 1096. IEEE.
[14] Mohammad, A.H., Alwada’n, T., Al-Momani, O. (2016). Arabic text categorization using support vector machine, naive bayes and neural network. GSTF Journal on Computing (JoC), 5(1), 108 (2016)
[15] Patel, D.B., Shah, S., Chhinkaniwala, H. R. (2019). Fuzzy logic based multi document summarization with improved sentence scoring and redundancy removal technique. Expert Systems with Applications.
[16] Saiyad, N. Y., Prajapati, H. B., Dabhi, V. K. (2016). A survey of document clustering using semantic approach. In: 2016
International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). p 2555–2562. IEEE.
[17] Sangaiah, A. K., Fakhry, A. E., Abdel-Basset, M., El-henawy, I. (2018). Arabic text clustering using improved clustering algorithms with dimensionality reduction. Cluster Computing, p 1–15.
[18] Taspýnar, M., Ganiz, M. C., Acarman, T. (2017). A feature based simple machine learning approach with word embeddings to named entity recognition on tweets. In: International Conference on Applications of Natural Language to Information Systems. p 254–259. Springer. |
