Üstverilerin Derin Öğrenme Algoritmaları Kullanılarak Otomatik Olarak Çıkartılması ve Sınıflanması

Yıl 2021, Cilt: 9 Sayı: 6 - ICAIAME 2021, 177 - 187, 31.12.2021

Murat İnce

https://doi.org/10.29130/dubited.1012046

Öz

Günümüzde bilişim teknolojilerinin yaygınlaşması sebebiyle dijital içerik ihtiyacı artmıştır. Bu içeriklerin oluşturulması zaman alıcı ve maliyetli bir süreçtir. İçerik oluşturulurken öğrenme nesnelerinden faydalanılmaktadır. Bu nesnelerin bilgisayarlar tarafından keşfedilebilir ve okunabilir olması yeniden kullanılabilirlik ve paylaşılabilirlik açısından önemlidir. Bu sebeple nesneler tanımlayıcı kimlik bilgilerini içeren üstveriler ile bütünleşik olarak kullanılmaktadırlar. Bu üstveriler ne kadar düzgün oluşturulup sınıflandırılırsa nesnelerin kullanılabilirliği o derece artmış olmaktadır. Bu sebeple nesnelerden otomatik üstveri çıkartan birçok yöntem geliştirilmiştir. Bu çalışmada da Konvolüsyonel Sinir Ağları (KSA), Tekrarlayan Sinir Ağları (TSA) gibi derin öğrenme ve Doğal Dil İşleme (DDİ) yöntemleri kullanılarak öğrenme nesnelerindeki içeriklerden otomatik olarak üstveri çıkartılması ve sınıflaması yapılmıştır. Sistemin başarısı ve doğruluğu örnek öğrenme nesneleri ile test edilmiştir. Sonuçlar sistemin başarılı bir şekilde kullanılabileceğini göstermiştir.

Anahtar Kelimeler

Üstveri çıkartma, Konvolüsyonel sinir ağları, Tekrarlayan sinir ağları

Automatic Metadata Extraction and Classification by using Deep Learning Algorithms

Öz

The need for digital content has increased due to the widespread use of information technologies today. Creating these contents is a time consuming and costly process. Learning objects are used while creating the content. It is important that these objects can be discovered and readable by computers in terms of reusability and shareability. For this reason, objects are used in integration with metadata containing identifying information. The more properly these metadata are created and classified, the greater the usability of the objects. For this reason, many methods have been developed that automatically extract metadata from objects. In this study, metadata extraction and classification from the contents of learning objects were made automatically by using deep learning methods such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Natural Language Processing (NLP). The success and accuracy of the system has been tested with sample learning objects. The results showed that the system can be used successfully.

Anahtar Kelimeler

Metadata extraction, Convolutional neural networks, Recurrent neural networks

Kaynakça

• [1] D. Jonassen, M. Davidson, M. Collins, J. Campbell, and B. B. Haag, “Constructivism and computer‐mediated communication in distance education,” American Journal of Distance Education, vol. 9, no. 2, pp. 7-26, 1995.
• [2] Y. Beldarrain, “Distance education trends: integrating new technologies to foster student interaction and collaboration,” Distance Education, vol. 27, no. 2, pp. 139-153, 2006.
• [3] Ö. F. Bay ve H. Tüzün, “Yüksek öğretim kurumlarında ders içeriğinin web tabanlı olarak aktarılması-I,” Politeknik Dergisi, c. 5, s. 1, ss. 13-22, 2002.
• [4] T. Yigit, A. H. Isik, and M. Ince, “Web-based Learning object selection software using analytical hierarchy process,” IET Software, vol. 8, no. 4, pp. 174-183, 2014.
• [5] L. Becksford and S. Metko, “Using a library learning object repository to empower teaching excellence for distance students,” Journal of Library & Information Services in Distance Learning, vol. 12, pp. 120-129, 2018.
• [6] K. Harman and A. Koohang, “Discussion board: a learning object,” Interdisciplinary Journal of E-Learning and Learning Objects, vol. 1, pp. 67–77, 2005.
• [7] J. Sinclair, M. Joy, Y. J. Yau, and S. Hagan, “A practice-oriented review of learning objects,” IEEE Transactions on Learning Technologies, vol. 6, pp. 177–192, 2013.
• [8] A. Zapata, V. H. Menéndez, M. E. Prieto, and C. Romero, “A framework for recommendation in learning object repositories: an example of application in civil engineering,” Advances inEngineering Software, vol. 56, pp. 1-13, 2013.
• [9] M. İnce, T. Yiğit, and A. H. Işık, “A hybrid AHP-GA method for metadata-based learning object evaluation,” Neural Computing and Applications, vol. 31, no. 1, pp. 671-681, 2019.
• [10] R. McGreal and T. Roberts, “A primer on metadata for learning objects: fostering an interoperable environment,” E-learning, vol. 2, no. 10, pp. 26-29, 2001.
• [11] P. Balatsoukas, A. Morris, and A. O’Brien, “Learning objects update: review and critical approach to content aggregation,” Educational Technology & Society, vol. 11, no. 2, pp. 119-130, 2008.
• [12] G. Millar, Learning Objects 101: A Primer for Neophytes. Learning Resources Unit, British Columbia Institute of Technology, 2002.
• [13] Y. Chen, “Educational resource management in grid community based on learning object metadata standard,” International Journal of Emerging Technologies in Learning, vol. 13, no. 11, pp. 130-143, 2018.
• [14] M. Knapp, Z. Risha, R. Gatewood, J. Van Der Volgen, R. Brown, and R. Kizilboga, “Learning to love the lor: implementing an internal learning object repository at a large national organization,” Medical Reference Services Quarterly, vol. 38, no. 2, pp. 143-155, 2019.
• [15] P. Brusilovsky, J. Eklund, and E. Schwarz, “Web-based education for all: a tool for development adaptive courseware,” Computer Networks and ISDN Systems, vol. 30, no. 1, pp. 291 300, 1998.
• [16] F. A. Dorça, R. D. Araújo, V. C. De Carvalho, D. T. Resend, and R. G. Cattelan, “An automatic and dynamic approach for personalized recommendation of learning objects considering students learning styles: an experimental analysis,” Informatics in Education, vol. 15, no. 1, pp. 45-62, 2016.
• [17] H. Imran, M. Belghis-Zadeh, T. W. Chang, and S. Graf, “PLORS: a personalized learning object recommender system,” Vietnam Journal of Computer Science, vol. 3, no. 1, pp. 3-13, 2016.
• [18] V. Dagiene, D. Gudoniene, and R. Bartkute, “The integrated environment for learning objects design and storing in semantic web,” International Journal of Computers, Communications &Control, vol. 13, no. 1, pp. 39-49, 2018.
• [19] F. Esposito, S. Ferilli, T. M. Basile, and N. Di Mauro, “Machine learning for digital document processing: from layout analysis to metadata extraction,” in Machine learning in document analysis and recognition, Berlin, Heidelberg: Springer, 2008, pp. 105-138.
• [20] S. Miranda and P. Ritrovato, “Supporting learning object repository by automatic extraction of metadata,” Journal of e-Learning and Knowledge Society, vol. 11, no. 1, pp. 43-54, 2015.
• [21] D. Roy, S. Sarkar, and S. Ghose, “Automatic extraction of pedagogic metadata from learning content,” International Journal of Artificial Intelligence in Education, vol. 18, no. 2, pp. 97-118, 2008.
• [22] E. Cortez, A. S. Da Silva, M. A. Gonçalves, F. Mesquita, and E. S. De Moura, “A flexible approach for extracting metadata from bibliographic citations,” Journal of the American Society for Information Science and Technology, vol. 60, no. 6, pp. 1144-1158, 2009.
• [23] K. Cardinaels, M. Meire, and E. Duval, “Automating metadata generation: the simple indexing interface,” in Proceedings of the 14th International Conference on World Wide Web, 2005, pp. 548 556.
• [24] H. Han, C. L. Giles, E. Manavoglu, H. Zha, Z. Zhang, and E. A. Fox “Automatic document metadata extraction using support vector machines,” in Joint Conference on Digital Libraries Proceedings, 2003, pp. 37-48.
• [25] P. Cimiano, S. Handschuh, and S. Staab, “Towards the self-annotating web,” in Proceedings of the 13th International Conference on World Wide Web, 2004, pp. 462-471.
• [26] B. Jebali and R. Farhat, “Ontology-based semantic metadata extraction approach,” in International Conference on Electrical Engineering and Software Applications, 2013, pp. 1-5.
• [27] W. Paik, S. Yilmazel, E. Brown, M. Poulin, S. Dubon, and C. Amice, “Applying natural language processing (nlp) based metadata extraction to automatically acquire user preferences,” in Proceedings of the 1st International Conference on Knowledge Capture, 2001, pp. 116-122.
• [28] P. Spinosa, G. Giardiello, M. Cherubini, S. Marchi, G. Venturi, and S. Montemagni, “NLP based metadata extraction for legal text consolidation,” in Proceedings of the 12th International Conference on Artificial Intelligence and Law, 2009, pp. 40-49.
• [29] R. Liu, L. Gao, D. An, Z. Jiang, and Z. Tang, “Automatic document metadata extraction based on deep networks,” in National CCF Conference on Natural Language Processing and Chinese Computing, 2017, pp. 305-317.
• [30] L. Deng and D. Yu, “Deep learning: methods and applications,” Foundations and Trends® in Signal Processing, vol. 7, pp. 197-387, 2014.
• [31] Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436 444, 2015.
• [32] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems, 2012, pp. 1097-1105.
• [33] J. Salamon and J. P. Bello, “Deep convolutional neural networks and data augmentation for environmental sound classification,” IEEE Signal Processing Letters, vol. 24, no. 3, pp. 279-283, 2017.
• [34] Y. J. Cha, W. Choi, and O. Büyüköztürk, “Deep learning‐based crack damage detection using convolutional neural networks,” Computer‐Aided Civil and Infrastructure Engineering, vol. 32, no. 5, pp. 361-378, 2017.
• [35] O. Vinyals, A. Toshev, S. Bengio, and D. Erhan, “Show and tell: lessons learned from the 2015 mscoco image captioning challenge,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 4, pp. 652-663, 2016.
• [36] A. V. D. Oord, N. Kalchbrenner, and K. Kavukcuoglu, “Pixel recurrent neural networks,” 2016, arXiv:1601.06759.
• [37] M. Schuster and K. K. Paliwal, “Bidirectional recurrent neural networks,” IEEE Transactions on Signal Processing, vol. 45, no. 11, pp. 2673-2681, 1997.
• [38] G. Toderici, D. Vincent, N. Johnston, S. Jin Hwang, D. Minnen, J. Shor, and M. Covell, “Full resolution image compression with recurrent neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 5306-5314.
• [39] A. Graves, “Generating sequences with recurrent neural networks,” 2013, arXiv:1308.0850.
• [40] A. Graves and J. Schmidhuber, “Offline handwriting recognition with multidimensional recurrent neural networks,” in Advances in Neural Information Processing Systems, 2009, pp. 545 552.
• [41] T. Hughes and K. Mierle, “Recurrent neural networks for voice activity detection,” in IEEE International Conference on Acoustics, Speech and Signal Processing, 2013, pp. 7378-7382.
• [42] S. K. Metin, T. Kisla, and B. Karaoglan, “Named entity recognition in Turkish using association measures,” Advanced Computing, vol. 3, no. 4, pp. 43-49, 2012.
• [43] M. H. Stefanini and Y. Demazeau “TALISMAN: a multi-agent system for natural language processing,” in Brazilian Symposium on Artificial Intelligence, 1995, pp. 312-322.
• [44] S. Sun, C. Luo, and J. Chen, “A review of natural language processing techniques for opinion mining systems,” Information Fusion, vol. 36, pp. 10-25, 2017.
• [45] T. Strzalkowski, F. Lin, J. Wang, and J. Perez-Carballo, “Evaluating natural language processing techniques in information retrieval,” in Natural Language Information Retrieval, Dordrecht: Springer, 1999, pp. 113-145.
• [46] T. Nasukawa and J. Yi, “Sentiment analysis: capturing favorability using natural language processing,” in Proceedings of the 2nd International Conference on Knowledge Capture, 2003, pp. 70-77.
• [47] Y. Aktaş, E. Y. İnce, and A. Çakır, “Doğal dil işleme kullanarak bilgisayar ağ terimlerinin wordnet ontolojisinde uyarlanması,” Teknik Bilimler Dergisi, c. 7, s. 2, ss. 1-9, 2019.
• [48] J. Cushing and R. Hastings, “Introducing computational linguistics with NLTK (natural language toolkit),” Journal of Computing Sciences in Colleges, vol. 25, no. 1, pp. 167-169, 2009.
• [49] S. Savaş and N. Topaloğlu, “Data analysis through social media according to the classified crime,” Turkish Journal of Electrical Engineering & Computer Sciences, vol. 27, no. 1, pp. 407-420, 2019.
• [50] E. Y. İnce, “Spell checking and error correcting application for Turkish,” International Journal of Information and Electronics Engineering, vol. 7, no. 2, pp. 68-71, 2017.
• [51] M. N. Al-Kabi, T. M. Hailat, E. M. Al-Shawakfa, and I. M. Alsmadi, “Evaluating English to Arabic machine translation using BLEU,” International Journal of Advanced Computer Science and Applications, vol. 4, no. 1, pp. 66-73, 2013.
• [52] S. Stoll, N. C. Camgöz, S. Hadfield, and R. Bowden, “Text2Sign: towards sign language production using neural machine translation and generative adversarial networks,” International Journal of Computer Vision, vol. 128, pp. 891-908, 2020.
• [53] T. Sing, O. Sander, N. Beerenwinkel, and T. Lengauer, “ROCR: visualizing classifier performance in R,” Bioinformatics, vol. 21, no. 20, pp. 3940-3941, 2005.
• [54] M. Hossin and M. N. Sulaiman, “A review on evaluation metrics for data classification evaluations,” International Journal of Data Mining & Knowledge Management Process, vol. 5, no. 2, pp. 1-11, 2005.
• [55] Coco. (2021, Aug 1). Ms-coco. [Online]. Available: https://cocodataset.org/#home