P. Shoval, V. Maidel, B. Shapira, “An Ontology- Content-Based Filtering Method”, International Journal "Information Theories & Applications", Vol. 15, pp.303-314, 2008.
 K. N. Junejo, A. Karim, M. T. Hassan, M. Jeon, “Terms-based discriminative information space for robust text classification”, Information Sciences, Vol. 372, pp.518-538, 2016.
 X. Zhang, X. Hou, X. Chen, T. Zhuang, “Ontology-based semantic retrieval for engineering domain knowledge”, Neurocomputing, Vol. 116, pp.382-391, 2013.
 Y. Jiang, W. Bai, X. Zhang, J. Hu, “Wikipedia-based information content and semantic similarity computation”, Information Processing & Management, Vol. 52, pp.248-265, 2017.
 B. Shapira, N. Ofek, and V. Makarenkov, “Exploiting Wikipedia for Information Retrieval Tasks”, In Proceedings of 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.1137-1140, 2015.
 O. Medelyan, D. Milne, C. Legg and I. Witten, "Mining meaning from Wikipedia", International Journal of Human-Computer Studies, Vol. 67, pp.716–754, 2009.
 H. K. Kim, H. Kim, and S. Cho, “Bag-of-concepts: Comprehending document representation through clustering words in distributed representation”, Neurocomputing, Vol. 266, pp.336-352, 2017.
 Y. Shen, X. He, J. Gao, L. Deng, and G. Mesnil, “A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval”, In Proceedings of CIKM, pp.101–110, 2014.
 H. Palangi, L. Deng, Y. Shen, J. Gao, X. He, J. Chen, X. Song And R. Ward, "Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval", IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 24, pp.694-707, 2016.
 V. Maidel, P. Shoval, B. Shapira, M. Taieb-Maimon, "Ontological content-based filtering for personalized newspapers: A method and its evaluation", Online Information Review, Vol. 34, pp.729 – 756, 2010.
 S. Kara, Ö. Alan, O. Sabuncu, S. Akpınar, N. K. Cicekli, F.N. Alpaslan, “An ontology-based retrieval system using semantic indexing”, Information Systems, Vol. 37, pp.294-305, 2012.
 G-J. Hahm, J-H. Lee, H-W. Suh, “Semantic relation based personalized ranking approach for engineering document retrieval”, Advanced Engineering Informatics, Vol. 29, pp.366-379, 2015.
 M. Daoud, L. Tamine, M. Boughanem, “A personalized search using a semantic distance measure in a graph-based ranking model”, Journal of Information Science, Vol. 37, pp.614–636, 2011.
 M. A. H. Taieb, M. B. Aouicha, A. B. Hamadou,”Computing semantic relatedness using Wikipedia features”, Knowledge-Based Systems”, Vol. 50, pp.260-278, 2013.
 P Malo, A. Sinha, J. Wallenius and P. Korhonen, "Concept-based Document Classification Using Wikipedia and Value Function", Journal of the American Society for Information Science and Technology, Vol. 62, pp.2496–2511, 2011.
 E. Gabrilovich and S. Markovitch, “Computing semantic relatedness using Wikipedia-based explicit semantic analysis”, In Proceedings of the 20th international joint conference on Artifical intelligence, pp.1606-1611, 2007.
 R. Navigli and S. P. Ponzetto, “Babelrelate! A Joint Multilingual Approach to Computing Semantic Relatedness”, In Proceedings of the 26th AAAI Conference on Artificial Intelligence, pp.108-114, 2012.
 Z. Wu, H. Zhu, G. Li, Z. Cui, H. Huang, J. Li, E. Chen, G. Xu, “An efficient Wikipedia semantic matching approach to text document classification”, Information Sciences, Vol. 393, 15-28, 2017.
 J. Gao, B. Zhang, X. Chen, “A WordNet-based semantic similarity measurement combining edge-counting and information content theory”, Engineering Applications of Artificial Intelligence, Vol. 39, pp.80-88, 2015.
 OntoWordNet Ontology, “Laboratory for applied ontology - DOLCE”, [last visited on Feb. 19, 2013], [Online], Available: <http://www.loa.istc.cnr.it/DOLCE.html# OntoWordNet >.
 J. G. Mersch, R. R. Lang, “An Information-Theoretic Sentence Similarity Metric”, In Proceedings of the 28th International Florida Artificial Intelligence Research Society Conference, pp.552-556, 2015.
 P. Kolb, “DISCO: A Multilingual Database of Distributionally Similar Words”, In Proceedings of KONVENS, pp.5-12, 2008.
 M. Warin, “Using WordNet and Semantic Similarity to Disambiguate an Ontology”, Thesis for the degree of Doctor of Philosophy, STOCKHOLMS University, Institute of Linguistic, 2004.
 C. Biemann, S. P. Ponzetto, S. Faralli, A. Panchenko, and E. Ruppert, “Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation.”, In Proceedings of EACL, 2017.
 W. Cohen, P. Ravikumar, S. Fienberg, “A comparison of string distance metrics for name-matching tasks”, In Proceedings of International Conference on Information Integration on the Web, pp.73-78, 2003.
 R. Thiagarajan, G. Manjunath and M. Stumptner, “Computing Semantic Similarity Using Ontologies”, HP Laboratories, 2008.
 L. Luna, R. Quintero, M. Torres, M. Moreno-Ibarra, G. Guzmán, I. Escamilla, “An ontology-based approach for representing the interaction process between user profile and its context for collaborative learning environments”, Computers in Human Behavior, Vol. 51, pp.1387-1394, 2015.
 D. Lin, "Extracting Collocations from Text Corpora", In Workshop on Computational Terminology, pp.57–63, 1998.
 L. Xu, S. Amar, “Combining Classifiers and Learning Mixture-of-Experts”, Encyclopaedia of Artificial Intelligence, Vol. 3, pp.318-326, 2009.
 S. Masoudnia and and R. Ebrahimpour, “Mixture of experts: a literature survey”, Artificial Intelligence Review, Vol. 42, pp.275-293, 2014.
 Ted Pedesen, “WordNet::Similarity”, [last visited on Sep. 29, 2016], [Online] Available: <http://wn-similarity.sourceforge.net/>.
 G.A. Miller and W.G. Charles, “Contextual correlates of semantic similarity”. Language and Cognitive Processes, Vol. 6, pp.1-28, 1991.
 D. Girardi, S. Wartner, G. Halmerbauer, M. Ehrenmüller, H. Kosorus, S. Dreiseitl, “Using concept hierarchies to improve calculation of patient similarity”, Journal of Biomedical Informatics, Vol. 63, pp.66-73, 2016.
 Mehmet Ali Salahli, “An Approach for Measuring Semantic Relatedness between Words via Related Terms”, Mathematical and Computational Applications, Vol. 14, pp.55-63, 2009.
 K. Lang, “The 20Newsgroups data set, version 20news-18828”, [last visited on Sep. 29, 2016], [Online] Available: <http://www. qwone.com/~jason/20Newsgroups>.
 W. Zhang, X. Tang, T. Yoshida, “TESC: An approach to TExt classification using Semi-supervised Clustering”, Knowledge-Based Systems, Vol. 75, pp.152-160, 2015.
 D. Bollegala, Y. Matsuo and M. Ishizuka, “Measuring semantic similarity between words using web search engines”, In Proceedings of the 16th international conference on World Wide Web, pp.757-766, 2007.
 R. Song, S. Chen, B. Deng, and L. Li, “eXtreme Gradient Boosting for Identifying Individual Users Across Different Digital Devices”, In Proceedings of WAIM, Vol. 9658, pp. 43–54, 2016.
 Q. Wu, Y. Ye, H. Zhang, M. Ng and S. Ho, “ForesTexter: An efficient random forest algorithm for imbalanced text categorization”, Knowledge-Based Systems, Vol. 67, pp.105-116, 2014.
 G. Rao, W. Huang, Z. Feng and Q. Cong, “LSTM with sentence representations for document-level sentiment classification”, Neurocomputing, Vol. 308, pp.49-57, 2018.
 M. Jiang, Y. Liang, X. Feng, X. Fan, Z. Pei, Y. Xue, and R. Guan, “Text classiﬁcation based on deep belief network and softmax regression,” Neural Computing and Applications, vol. 29, no. 1, pp. 61–70, 2018.
 C.-H. Shih, B.-C. Yan, S.-H. Liu, and B. Chen, “Investigating siamese lstm networks for text categorization,” in 2017 Asia-Paciﬁc Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2017, pp. 641–646.
 J. Camacho-Collados and M. T. Pilehvar, “On the role of text preprocessing in neural network architectures: An evaluation study on text categorization and sentiment analysis,” arXiv preprint arXiv:1707.01780, 2017.
 D. Wang, C. Lu, J. Wu, H. Liu, W. Zhang, F. Zhuang, and H. Zhang, “Softly associative transfer learning for cross-domain classiﬁcation,” IEEE transactions on cybernetics, 2019.
 , M. N. Asim, M. U. G. Khan, M. I. Malik, A. Dengel, S. Ahmed, “A Robust Hybrid Approach for Textual Document Classification”., arXiv preprint arXiv:1909.05478, 2019.
 K. Kowsari, M. Heidarysafa, D. E. Brown, K. J. Meimandi, L. Barnes, “Rmdl: Random multimodel deep learning for classification”, In Proceedings of the 2nd International Conference on Information System and Data Mining, pp. 19-28, 2018.
 M. B. Revanasiddappa, B. S. Harish, S. Manjunath, “Document classification using symbolic classifiers”, In 2014 International Conference on Contemporary Computing and Informatics, pp. 299-303, 2014.