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Topic Segmentation and Evaluation Measures for E-learning based on Domain and Pedagogical Ontology

by K.Sathiyamurthy, T.V.Geetha
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 26 - Number 6
Year of Publication: 2011
Authors: K.Sathiyamurthy, T.V.Geetha
10.5120/3110-4270

K.Sathiyamurthy, T.V.Geetha . Topic Segmentation and Evaluation Measures for E-learning based on Domain and Pedagogical Ontology. International Journal of Computer Applications. 26, 6 ( July 2011), 5-10. DOI=10.5120/3110-4270

@article{ 10.5120/3110-4270,
author = { K.Sathiyamurthy, T.V.Geetha },
title = { Topic Segmentation and Evaluation Measures for E-learning based on Domain and Pedagogical Ontology },
journal = { International Journal of Computer Applications },
issue_date = { July 2011 },
volume = { 26 },
number = { 6 },
month = { July },
year = { 2011 },
issn = { 0975-8887 },
pages = { 5-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume26/number6/3110-4270/ },
doi = { 10.5120/3110-4270 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:12:03.977696+05:30
%A K.Sathiyamurthy
%A T.V.Geetha
%T Topic Segmentation and Evaluation Measures for E-learning based on Domain and Pedagogical Ontology
%J International Journal of Computer Applications
%@ 0975-8887
%V 26
%N 6
%P 5-10
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper discusses the UNL Enconversion of Tamil sentences. The rich morphology of Tamil enables the Enconversion process to be based on morpho-semantic features of the words and their preceding and succeeding context. The use of case relation indicating morphological suffixes, POS tag and word level semantics allows the rule based Enconversion to be independent of the syntactic structure of the sentence. These UNL graphs are used to build a conceptual level index.

References
  1. The ACM Computing Classification System–1998 Version valid in 2009, http://www.acm.org/class/1998/
  2. D. Beeferman, A. Berger, and J. Lafferty. Statistical models for text segmentation. Machine Learning, 34 (1–3):177–210, 1999.
  3. Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers, Text Modeling using Unsupervised Topic Models and Concept Hierarchies , CoRR abs/0808.0973: (2008)
  4. David M. Blei ,Andrew Y. Ng ,Michael I. Jordan ,Latent Dirichlet Allocation, Journal of Machine Learning Research, 3:993–1022, 2003.
  5. David M. Blei ,Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum, Hierarchical Topic Models and the Nested Chinese Restaurant Process, Advances in Neural Information Processing Systems , NIPS, December 8-13, 2003.
  6. David Mimno Andrew McCallum ,Mixtures of Hierarchical Topics with Pachinko Allocation, Proceedings of the Twenty-Fourth International Conference (ICML 2007), Corvalis, Oregon, USA, June 20-24, 2007, 633-640
  7. Hemant Misra,François Yvon,Joemon M. Jose, Text Segmentation via Topic Modeling: An Analytical Study, Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, Hong Kong, China, November 2-6, 2009
  8. Lev Pevzner Marti A. Hearsty, A Critique and Improvement of an Evaluation Metric for Text Segmentation, Computational Linguistics, 28 (1):19–36, 2002
  9. Marti Hearst. 1997. TextTiling: Segmenting Text into Multi-Paragraph Subtopic Passages, Computational Linguistics, and 23(1):33–64.
  10. M. Mahdi Shafiei and Evangelos E. Milios A Statistical Model for Topic Segmentation and Clustering, Canadian Conference on AI 2008: 283-295
  11. Maria Georgescul,Alexander Clark & Susan Armstrong, A Comparative Study of Mixture Models for Automatic Topic Segmentation of Multiparty Dialogues, The 3rd International Joint Conference on Natural Language Processing (IJCNLP), Hyderabad, India, January 7-12, 2008.
  12. Qi Sun, Runxin Li, Dingsheng Luo and XihongWu, Text Segmentation with LDA-Based Fisher Kernel, Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, June 15-20, 2008, Columbus, Ohio, USA.
  13. Sathiyamurthy.K, T.V.Geetha, Association of domain concepts with Educational objectives for E-Learning, ACM compute 2010, Bangalore, India.
  14. Sathiyamurthy.K, T.V.Geetha, Topic Segmentation of E-Learning materials using Domain and Pedagogical Ontology, ICFIT December 2010, Changsa, China.
  15. Stojanovic, L. , Staab, S., and Studer, R., Elearning based on the Semantic Web, World Conference on the WWW and Internet (WebNet), Orlando, Florida, USA, 2001
  16. Takayuki Sekiya, Yoshitatsu Matsuda and Kazunori Yamaguchi, Analysis of Curriculum Structure Based on LDA, International Conference on Education and Information Technology 2009, pp.561-566.
  17. Wei Li weili,Andrew McCallum Pachinko Allocation: DAG-Structured Mixture Models of Topic Correlations, Proceedings of the Twenty-Third International Conference (ICML 2006), Pittsburgh, Pennsylvania, USA, June 25-29, 2006. ICML 2006: 577-584
  18. Wang Wei, Payam Barnaghi and Andrzej Bargiela, Probabilistic Topic Models for Learning Terminological Ontologies, IEEE Transactions on Knowledge and Data Engineering , 22(7): 1028-1040 (2010).
Index Terms

Computer Science
Information Sciences

Keywords

Latent Dirichlet Allocation (LDA) Ontology Pedagogy

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