Abstract
Finding appropriate e-Learning resources within a repository of videos represents a critical aspect for students. Given that transcripts are available for the entire set of videos, the problem reduces to obtaining a ranked list of video transcripts for a particular query. The paper presents a custom approach for searching the 16.012 available video transcripts from https://media.upv.es/ at Universitat Politècnica de València. An inherent difficulty of the problem comes from the fact that transcripts are in the Spanish language. The proposed solution embeds all the transcripts using feed-forward Neural-Net Language Models, clusters the embedded transcripts and builds a Latent Dirichlet Allocation (LDA) model for each cluster. We can then process a new query and find the transcripts that have the LDA results closest to the LDA results for our query.
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References
Aman Srivastava: LSTM Siamese Text Similarity, April 2019. https://github.com/amansrivastava17/lstm-siamese-text-similarity
Baeza-Yates, R., Ribeiro, B.D.A.N., et al.: Modern information retrieval. ACM Press, New York. Addison-Wesley, Harlow (2011)
Bakar, Z.A., Kassim, M., Sahroni, M.N., Anuar, N.: A survey: framework to develop retrieval algorithms of indexing techniques on learning material. J. Telecommun. Electron. Comput. Eng. (JTEC) 9(2–5), 43–46 (2017)
Basu, S., Yu, Y., Singh, V.K., Zimmermann, R.: Videopedia: lecture video recommendation for educational blogs using topic modeling. In: Tian, Q., Sebe, N., Qi, G.-J., Huet, B., Hong, R., Liu, X. (eds.) MMM 2016. LNCS, vol. 9516, pp. 238–250. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-27671-7_20
Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3(Feb), 1137–1155 (2003)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)
Budnik, M., Gutierrez-Gomez, E.L., Safadi, B., Quénot, G.: Learned features versus engineered features for semantic video indexing. In: 2015 13th International Workshop on Content-Based Multimedia Indexing (CBMI), pp. 1–6. IEEE (2015)
Chen, H., Cooper, M., Joshi, D., Girod, B.: Multi-modal language models for lecture video retrieval. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 1081–1084. ACM (2014)
Elleuch, N., Ammar, A.B., Alimi, A.M.: A generic framework for semantic video indexing based on visual concepts/contexts detection. Multimedia Tools Appl. 74(4), 1397–1421 (2015)
Iyer, R.R., Parekh, S., Mohandoss, V., Ramsurat, A., Raj, B., Singh, R.: Content-based video indexing and retrieval using corr-lda. arXiv preprint arXiv:1602.08581 (2016)
MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA, vol. 1, pp. 281–297 (1967)
Ngo, C.W., et al.: Experimenting VIREO-374: bag-of-visual-words and visual-based ontology for semantic video indexing and search. In: TRECVID (2007)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)
Pelleg, D., Moore, A.W., et al.: X-means: Extending k-means with efficient estimation of the number of clusters. In: ICML, vol. 1, pp. 727–734 (2000)
Repp, S., Grob, A., Meinel, C.: Browsing within lecture videos based on the chain index of speech transcription. IEEE Trans. Learn. Technol. 1(3), 145–156 (2008)
Crayston, T.: The Natural Language Processing API, April 2019. https://www.textrazor.com/technology
Van Nguyen, N., Coustaty, M., Ogier, J.M.: Multi-modal and cross-modal for lecture videos retrieval. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 2667–2672. IEEE (2014)
Yang, H., Meinel, C.: Content based lecture video retrieval using speech and video text information. IEEE Trans. Learn. Technol. 2, 142–154 (2014)
Yousef, A.M.F., Chatti, M.A., Schroeder, U.: Video-based learning: a critical analysis of the research published in 2003–2013 and future visions (2014)
Acknowledgements
This work was partially supported by the project TIN2017-89156-R of the Spanish government, and by the grant program for the recruitment of doctors for the Spanish system of science and technology (PAID-10-14) of the Universitat Politècnica de València.
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Turcu, G., Heras, S., Palanca, J., Julian, V., Mihaescu, M.C. (2019). Towards a Custom Designed Mechanism for Indexing and Retrieving Video Transcripts. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_26
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