KATEGORISASI DAN ANOTASI BAHAN PEMBELAJARAN MENGGUNAKAN PENDEKATAN KESAMAAN TEKS

Subhan A Gani

Abstract


Penggunaan kembali bahan pembelajaran dalam sistem e-learning menjadi isu penting karena biaya pembuatan bahan e-learning itu mahal. Memperluas cakupan penggunaan bahan pembelajaran untuk bidang tertentu dapat dicapai dengan membubuhi keterangan (anotasi) di dalam metadata Bahan Pembelajaran (Learning Object, yang selanjutnya disingkat: LO) secara semantik sesuai dengan deskripsi bidang yang diminati oleh pengguna. Penelitian ini bermaksud untuk memperluas penggunaan kembali LO dari sebuah repositori e-learning berbahasa Inggris agar dapat digunakan sebagai materi tambahan pembelajaran mandiri melalui sistem e-learning dalam program pelatihan tenaga kerja. Kemiripan topik sebuah LO dihitung secara kuantitatif menggunakan pendekatan Kesamaan Tingkat Lexikal dengan memanfaatkan database WordNet sebagai basis kesamaan leksikal. Pendekatan ini dimaksudkan untuk mengkategorikan topik LO dengan topik deskripsi pekerjaan seorang peserta pelatihan dengan cara mengukur skor kesamaan antara makna tekstual dari judul, deskripsi, dan kata kunci LO dengan judul, deskripsi, dan kata kunci dalam deskripsi pekerjaan tertentu. Tujuan penelitian ini adalah untuk mengevaluasi tingkat akurasi pendekatan Kesamaan Tingkat Lexikal dalam menyimpulkan kesamaan topik dari LO. Berdasarkan percobaan, pendekatan ini memberikan tingkat akurasi yang rendah ketika menemukan dua topik yang serupa, namun mempunyai tingkat akurasi yang tinggi dalam menemukan dua topik yang berbeda.

Keywords


Learning Object, metadata annotation, e-learning repository, text categorization, Natural Language Processing.

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References


K. Cardinaels, M. Meire, dan E. Duval, Automating metadata generation: the simple indexing interface, Proc. 14th International Conference on World Wide Web, 2005, pp. 548-556.

D. G. Sampson. Competence-related metadata for educational resources that support lifelong competence development programmes. Educational Technology & Society, vol. 12, pp. 149-159, 2009.

B. Chang, Y. Lee, S. Ko, dan J. Cha, Enhancing ontology-based educational content search service with competency, Eighth IEEE International Conference on Advanced Learning Technologies, ICALT'08., 2008, pp. 293-294.

E. Duval dan W. Hodgins, A LOM research agenda, Proc. 12th International Conference on World Wide Web, 2003, pp. 1-9.

J. Greenberg, A. Crystal, W. D. Robertson, dan E. Leadem, Iterative design of metadata creation tools for resource authors, International Conference on Dublin Core and Metadata Applications, 2003, pp. pp. 49-58.

I. L. T. S. Committee, Learning Object Metadata, Final Draft Standard, IEEE 1484.12. 1-2002, Available in: http://ltsc.ieee.org/wg12/20020612-Final-LOM-Draft.html, 2002.

A. Gelbukh, G. Sidorov, dan A. Guzman-Arenas, Document comparison with a weighted topic hierarchy, Proc. Tenth International Workshop on Database and Expert Systems Applications, 1999, pp. 566-570.

J. Jovanović, D. Gašević, dan V. Devedžić. Automating semantic annotation to enable learning content adaptation. Adaptive Hypermedia and Adaptive Web-Based Systems, 2006, pp. 151-160.

D. Roy, S. Sarkar, dan S. Ghose. A Comparative Study of Learning Object Metadata, Learning Material Repositories, Metadata Annotation & an Automatic Metadata Annotation Tool. Advances in Semantic Computing, vol. 2, 2010.

C. Fellbaum dan G. Miller, Wordnet: An electronic lexical database, (ed: MIT Press, 1998).

J. Boyd-Graber, C. Fellbaum, D. Osherson, dan R. Schapire, Adding dense, weighted connections to wordnet, Proc. 3rd International WordNet Conference, 2006, pp. 29-36.

Q. Do, D. Roth, M. Sammons, Y. Tu, dan V. G. V. Vydiswaran. Robust, light-weight approaches to compute lexical similarity. Computer Science Research and Technical Reports, University of Illinois. http://hdl. handle. net/2142/15462, 2010.

P. Achananuparp, X. Hu, dan X. Shen. The evaluation of sentence similarity measures. Data Warehousing and Knowledge Discovery. pp. 305-316, 2008.

J. J. Rocchio, Relevance Feedback in Information Retrieval, in G. Salton (Ed.), The SMART Retrieval System - Experiments in Automatic Document Processing (Prentice Hall, 1971).

M. Lesk, Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone, Proceedings of the 5th annual international conference on Systems documentation, Toronto, Ontario, Canada, 1986.

H. Schütze. Automatic word sense discrimination. Computational linguistics, vol. 24, pp. 97-123, 1998.

G. Salton, A. Singhal, M. Mitra, dan C. Buckley. Automatic text structuring and summarization. Information Processing & Management, vol. 33, pp. 193-207, 1997.

K. Papineni, Why inverse document frequency?, Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies, Pittsburgh, Pennsylvania, 2001.

C.-Y. Lin dan E. Hovy, Automatic evaluation of summaries using n-gram co-occurrence statistics, Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1, 2003, pp. 71-78.

M. Lapata dan R. Barzilay, Automatic evaluation of text coherence: Models and representations, International Joint Conference On Artificial Intelligence, 2005, p. 1085.

P. Achananuparp, X. Hu, X. Zhou, dan X. Zhang, Utilizing sentence similarity and question type similarity to response to similar questions in knowledge-sharing community, 17th International Conference on World Wide Web, 2008.

B. Dolan, C. Quirk, dan C. Brockett, Unsupervised construction of large paraphrase corpora: Exploiting massively parallel news sources, Proceedings of the 20th international conference on Computational Linguistics, 2004, p. 350.

I. Dagan, O. Glickman, dan B. Magnini. The pascal Recognising Textual Entailment Challenge. Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment, pp. 177-190, 2006.

G. A. Miller. WordNet: a lexical database for English. Communications of the ACM. vol. 38, pp. 39-41, 1995.

Z. Wu dan M. Palmer, Verbs semantics and lexical selection, Proceedings of the 32nd annual meeting on Association for Computational Linguistics, 1994, pp. 133-138.

Technical Guide of Accessing Credit Point of Computer Administrator, S. C. Body 16/2008, 2008.

Y. Tu dan D. Roth. Learning English light verb constructions: contextual or statistical. ACL HLT 2011, p. 31, 2011.

H. Hamilton. Computer Science 831: Knowledge Discovery in Databases. 2000

D. Roth dan D. Zelenko, Part of speech tagging using a network of linear separators, Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics-Volume 2, 1998, pp. 1136-1142.

K. Celik, A Comprehensive Analysis of Using Wordnet, Part-Of-Speech Tagging, and Word Sense Disambiguation in Text Categorization, Ph.D. dissertation, Bogaziçi University, 2012.

B. Spell. (2009, March 20, 2013). Java API for WordNet Searching (JAWS). Available: http://lyle.smu.edu/~tspell/jaws/index.html

R. Kohavi dan F. Provost. Glossary of terms: Editorial for the Special Issue on Applications of Machine Learning and the Knowledge Discovery Process. Journal of Machine Learning, vol. 30, 1998.

V. Adamchik, Algorithmic Complexity, ed. http://www.cs.cmu.edu/~adamchik/15-121/lectures/Algorithmic%20Complexity/complexity.html, 2009.




DOI: https://doi.org/10.53912/iejm.v9i1.501

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