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Semantic Classification and Indexing of Open Educational Resources with Word Embeddings and Ontologies

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Cybernetics and Information Technologies
Special issue on Innovations in Intelligent Systems and Applications
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1. Eichhorn, S., G. W. Matkin. Massive Open Online Courses, Big Data, and Education Research. – New Directions for Institutional Research, Vol. 167, 2015, Wiley, 2016, pp. 27-40.10.1002/ir.20152 Search in Google Scholar

2. Mao, Y., Z. Lu. MeSH Now: Automatic MeSH Indexing at PubMed Scale via Learning to Rank. – J. Biomed Semantics, Vol. 17, April 2017, 8(1):15. DOI: 10.1186/s13326-017-0123-3.10.1186/s13326-017-0123-3539296828412964 Search in Google Scholar

3. Koutsomitropoulos, D. A., G. D. Solomou, A. K. Kalou. Federated Semantic Search Using Terminological Thesauri for Learning Object Discovery. – International Journal of Enterprise Information Management, Vol. 30, Emerald, 2017, No 5, pp. 795-808.10.1108/JEIM-06-2016-0116 Search in Google Scholar

4. Koutsomitropoulos, D. A., G. D. Solomou. A Learning Object Ontology Repository to Support Annotation and Discovery of Educational Resources Using Semantic Thesauri. – IFLA Journal SAGE, Vol. 44, 2018, No 1, pp. 4-24.10.1177/0340035217737559 Search in Google Scholar

5. Europe PMC Consortium. Europe PMC: A Full-Text Literature Database for the Life Sciences and Platform for Innovation. – Nucleic Acids Research, Vol. 43, 11 August 2017. Database Issue (2015): D1042-D1048. PMC. Web.10.1093/nar/gku1061438390225378340 Search in Google Scholar

6. McMartin, F. MERLOT: A Model for User Involvement in Digital Library Design and Implementation. – Journal of Digital Information, Vol. 5, 2006, No 3. Search in Google Scholar

7. U. S. National Library of Medicine. Medical Subject Headings, 2019. https://www.nlm.nih.gov/mesh/meshhome.html Search in Google Scholar

8. Koutsomitropoulos, D., A. Andriopoulos, S. Likothanassis. Subject Classification of Learning Resources Using Word Embeddings and Semantic Thesauri. – In: Proc. of IEEE Innovations in Intelligent Systems and Applications 2019 (INISTA’19), Sofia, Bulgaria, 3-5 July 2019.10.1109/INISTA.2019.8778377 Search in Google Scholar

9. Mikolov, T., K. Chen, G. Corrado, J. Dean. Efficient Estimation of Word Representations in Vector Space. – In: ICLR Workshop, 2013. Search in Google Scholar

10. Le, Q.,V. T. Mikolov. Distributed Representations of Sentences and Documents. – In: Proc. of 31st International Conference on Machine Learning (ICML’14), 2014. Search in Google Scholar

11. Mandelbaum, A., A. Shalev. Word Embeddings and Their Use in Sentence Classification Tasks. – In: CoRR, Cornel University, arxiv.org/abs/160.08229, October 2016. Search in Google Scholar

12. Turner, C. A., A. D. Jacobs, C. K. Marques, J. C. Oates, D. L. Kamen, P. E. Anderson, J. S. Obeid. Word2Vec Inversion and Traditional Text Classifiers for Phenotyping Lupus. – BMC in Medical Informatics and Decision Making, Vol. 17, January 2017, pp. 126-136.10.1186/s12911-017-0518-1556829028830409 Search in Google Scholar

13. Liu, Q., H. Huang, Y. Gao, X. Wei, Y. Tian, L. Liu. Task-Oriented Word Embedding for Text Classification. COLING, 2018. Search in Google Scholar

14. Suraj, S., V. Deepali. Unsupervised Text Classification and Search Using Word Embeddings on a Self-Organizing Map. – International Journal of Computer Applications. Vol. 156, December 2016, pp. 35-37. DOI: 10.5120/ijca2016912570.10.5120/ijca2016912570 Search in Google Scholar

15. Stein, R. A., P. A. Jaques, J. F. Valiati. An Analysis of Hierarchical Text Classification Using Word Embeddings. – Information Sciences, Vol. 471, 2019, pp. 216-232.10.1016/j.ins.2018.09.001 Search in Google Scholar

16. Petrolito, R., F. D. Orletta. Word Embeddings in Sentiment Analysis. – In: Proc. of 6th Italian Conference on Computational Linguistics (CLiC-it 2018), Vol. 2253, Torino, Italy, 2018.10.4000/books.aaccademia.3589 Search in Google Scholar

17. Petrolito, R., F. D. Orletta. Document Retrieval and Question Answering in Medical Documents. A Large-Scale Corpus Challenge. – In: Proc. of Biomedical NLP Workshop Associated with RANLP, Varna, Bulgaria, September 2017, pp. 1-7. Search in Google Scholar

18. Meilin, Z. Research on Text Classification Method Based on Multi-Type Classifier Fusion. – In: Proc. of 8th International Conference on Social Network, Communication and Education (SNCE’18), Shenyang, China, Vol. 83, May 2018, pp. 798-805. Search in Google Scholar

19. Wang, R., W. Liu, C. McDonald. Corpus-Independent Generic Keyphrase Extraction Using Word Embedding Vectors. – In: Proc. of Software Engineering Research Conference, Vol. 39, 2014. Search in Google Scholar

20. Wang, R., W. Liu, C. McDonald. Using Word Embeddings to Enhance Keyword Identification for Scientific Publications. – In: Proc. of 26th Australasian Database Conference, ADC’2015, Melbourne, Australia. Springer, June 2015, pp. 257-268.10.1007/978-3-319-19548-3_21 Search in Google Scholar

21. Mahata, D., J. Kuriakose, R. R. Shah, R. Zimmermann, J. R. Talburt. Theme-Weighted Ranking of Keywords from Text Documents Using Phrase Embeddings. – In: Proc. of IEEE Conference on Multimedia Information Processing and Retrieval (MIPR’18), Miami, USA, April 2018, pp. 184-189.10.31219/osf.io/tkvap Search in Google Scholar

22. Chen, S., A. Soni, A. Pappu, Y. Mehdad. DocTag2Vec: An Embedding Based Multi-Label Learning Approach for Document Tagging. – In: Proc. of 2nd Workshop on Representation Learning for NLP, Vancouver, Canada, August 2017, pp. 111-120.10.18653/v1/W17-2614 Search in Google Scholar

23. Peters, M. E., M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, L. Zettlemoyer. Deep Contextualized Word Representations. arXiv:1802.05365v2 [cs.CL], NAACL, March 2018.10.18653/v1/N18-1202 Search in Google Scholar

24. Sheikhshabbafghi, G., I. Birol, A. Sarkar. In-Domain Context-Aware Token Embeddings Improve Biomedical Named Entity Recognition. – In: Proc. of 9th International Workshop on Health Text Mining and Information Analysis (LOUHI’18), Brussels, Belgium, October 2018, pp. 160-164. DOI: 10.18653/v1/W18-5618.10.18653/v1/W18-5618 Search in Google Scholar

25. Martínez-Romero, M., C. Jonquet, M. J. O’Connor, J. Graybeal, A. Pazos, M. A. Musen. NCBO Ontology Recommender 2.0: An Enhanced Approach for Biomedical Ontology Recommendation. – Journal of Biomedical Semantics, Vol. 8, 2017, No 1, Article No 21. DOI:10.1186/s13326-017-0128-y.10.1186/s13326-017-0128-y546331828592275 Search in Google Scholar

26. Peng, S., R. You, H. Wang, C. Zhai, H. Mamitsuka, S. Zhu. DeepMeSH: Deep Semantic Representation for Improving Large-Scale MeSH Indexing. – Bioinformatics, 15;32, June 2016, Article No 12, pp. i70-i79. DOI: 10.1093/bioinformatics/btw294.10.1093/bioinformatics/btw294490836827307646 Search in Google Scholar

27. Kosmopoulos, A., I. Androutsopoulos, G. Paliouras. Biomedical Semantic Indexing Using Dense Word Vectors in BioASQ. – J. BioMed Semant Suppl BioMedl Inf Retr, 2015. Search in Google Scholar

28. Abdeddaïm, S., S. Vimard, L. F. Soualmia. The MeSH-Gram Neural Network Model: Extending Word Embedding Vectors with MeSH Concepts for UMLS Semantic Similarity and Relatedness in the Biomedical Domain. arXiv:1812.02309v1 [cs.CL], November 2018. Search in Google Scholar

29. Segura, B., P. Martínez, M. A. Carruan. Search and Graph Database Technologies for Biomedical Semantic Indexing: Experimental Analysis. – JMIR Med Inform. 1;5, December 2017, (4): e48. DOI: 10.2196/medinform.7059.10.2196/medinform.7059573232929196280 Search in Google Scholar

30. Ternier, S., K. Verbert, G. Parra, B. Vandeputte, J. Klerkx, E. Duval et al. The Ariadne Infrastructure for Managing and Storing Metadata. – IEEE Internet Computing, Vol. 13, 2009, No 4.10.1109/MIC.2009.90 Search in Google Scholar

31. A. Miles, S. Bechhofer, Eds. SKOS Simple Knowledge Organization System Reference. W3C Recommendation, 2009. http://www.w3.org/TR/skos-reference Search in Google Scholar

32. Schnabel, T., I. Labutov, D. M. Mimno, T. Joachims. Evaluation Methods for Unsupervised Word Embeddings. – In: Proc. of Conference on Empirical Methods in Natural Language Processing (EMNLP’15), Lisbon, Portugal, September 2015, pp. 298-307.10.18653/v1/D15-1036 Search in Google Scholar

33. Assem, V. M., V. Malaisé, A. Miles, G. Schreiber. A Method to Convert Thesauri to SKOS. – In: Proc. of 3rd European Semantic Web Conference of the Semantic Web, Research and Applications, ESWC’2006, Budva, Montenegro, 11-14 June 2006, Vol. 4011, Springer, 2006, p. 95.10.1007/11762256_10 Search in Google Scholar

34. U.S. Department of Health & Human Services, MEDLINE®PubMed® XML Element Descriptions and their Attributes, 2018. https://www.nlm.nih.gov/bsd/licensee/elements_descriptions.html Search in Google Scholar

35. Zhang, E., Y. Zhang. Average Precision. – In: L. Liu, M. T. Özsu, Eds. Encyclopedia of Database Systems. Springer, Boston, 2009, MA.10.1007/978-0-387-39940-9_482 Search in Google Scholar

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