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Reseach Article

Semantic Attributes Model for Automatic Generation of Multiple Choice Questions

by Ibrahim Eldesoky Fattoh, Amal Elsayed Aboutabl, Mohamed Hassan Haggag
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 103 - Number 1
Year of Publication: 2014
Authors: Ibrahim Eldesoky Fattoh, Amal Elsayed Aboutabl, Mohamed Hassan Haggag
10.5120/18038-8544

Ibrahim Eldesoky Fattoh, Amal Elsayed Aboutabl, Mohamed Hassan Haggag . Semantic Attributes Model for Automatic Generation of Multiple Choice Questions. International Journal of Computer Applications. 103, 1 ( October 2014), 18-24. DOI=10.5120/18038-8544

@article{ 10.5120/18038-8544,
author = { Ibrahim Eldesoky Fattoh, Amal Elsayed Aboutabl, Mohamed Hassan Haggag },
title = { Semantic Attributes Model for Automatic Generation of Multiple Choice Questions },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 103 },
number = { 1 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 18-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume103/number1/18038-8544/ },
doi = { 10.5120/18038-8544 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:33:25.740015+05:30
%A Ibrahim Eldesoky Fattoh
%A Amal Elsayed Aboutabl
%A Mohamed Hassan Haggag
%T Semantic Attributes Model for Automatic Generation of Multiple Choice Questions
%J International Journal of Computer Applications
%@ 0975-8887
%V 103
%N 1
%P 18-24
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this research, an automatic multiple choice question generation system for evaluating semantic role labels and named entities is proposed. The selection of the informative sentence and the keyword to be asked about are based on the semantic labels and named entities that exist in the question sentence. The research introduces a novel method for the distractor selection process. Distractors are chosen based on a string similarity measure between sentences in the data set. Eight algorithms of string similarity measures are used in this research. The system is tested using a set of sentences extracted from the data set for question answering. Experimental results prove that the semantic role labeling and named entity recognition approaches can be used for keyword selection. String similarity measures have been used in generating the distractors in the process of automatic multiple choice questions generation. Combining the similarity measures of some algorithms led to enhancing the results.

References
  1. Chen, C. Y. , Liou, H. C. , & Chang, J. S. (2006, July). Fast: an automatic generation system for grammar tests. In Proceedings of the COLING/ACL on Interactive presentation sessions (pp. 1-4). Association for Computational Linguistics.
  2. Brown, J. , Firshkoff, G. And Eskenazi, M. (2005) Automatic Question Generation For Vocabulary Assessment. Proceedings OfHlt/Emnlp, 819–826. Vancuver, Canada.
  3. Sung, L. , Lin, Y. , And Chern, M. (2007). An Automatic Quiz Generation System For English Text. Seventh Ieee International Conference On Advanced Learning Technologies.
  4. Lin, Y. , Sung, L. , AndChern, M (2007). An Automatic Multiple-Choice Question Generation Scheme For English Adjective Understanding. Workshop On Modeling, Management And Generation Of Problems/Questions In Elearning, The 15th International Conference On Computers In Education (Icce 2007), Pages 137-142, Hiroshima, Japan.
  5. Agarwal , M. And Mannem ,P. (2011). Automatic Gap-Fill Question Generation From Text Books. In Proceedings Of The 6th Workshop On Innovative Use Of Nlp For Building Educational Applications. Portland, Or, Usa. Pages 56-64.
  6. Trandab??, D. Using semantic roles to improve summaries. (2007). In The 13th European Workshop on Natural Language Generation (p. 164).
  7. PizzatoL. , and Molla D. (2008). Indexing on Semantic Roles for Question Answering. Coling 2008: Proceedings of the 2nd workshop on Information Retrieval for Question Answering (IR4QA). Pages 74–81. Manchester, UK.
  8. Palmer M. , Gildea D. , and Kingsbury P. (2005). The proposition bank: An annotated corpus of semantic roles. Computational Linguistics, 31(1):71–106.
  9. Tkachenko M. , and Simanovisky A. (2012). Named Entity Recognition: Exploring Features. Proceedings of KONVENS 2012 (Main track: oral presentations), Vienna.
  10. Gomaa, W. H. And Fahmy, A. A. (2014). Automatic Scoring for Answers to Arabic Test Questions. Computer Speech & Language 28 (4), 833-857.
  11. Smith, F. T. & Waterman, S. M. (1981). Identification of Common Molecular Subsequences, Journal of Molecular Biology 147: 195–197.
  12. Hall, P. A. V. & Dowling, G. R. (1980) Approximate string matching, Comput. Surveys, 12:381-402.
  13. Peterson, J. L. (1980). Computer programs for detecting and correcting spelling errors, Comm. Assoc. Comput. Mach. , 23:676-687.
  14. Jaro, M. A. (1989). Advances in record linkage methodology as applied to the 1985 census of Tampa Florida, Journal of the American Statistical Society, vol. 84, 406, pp 414-420.
  15. Jaro, M. A. (1995). Probabilistic linkage of large public health data file, Statistics in Medicine 14 (5-7), 491-8.
  16. Dice, L. (1945). Measures of the amount of ecologic association between species. Ecology, 26(3).
  17. Jaccard, P. (1901). Étude comparative de la distribution floraledansune portion des Alpes et des Jura. Bulletin delaSociétéVaudoise des Sciences Naturelles 37, 547-579.
  18. Eugene F. K. (1987). Taxicab Geometry, Dover. ISBN 0-486-25202-7.
  19. Chapman, S. (2009). Simmetrics: a java & c#. Net library of similarity metrics.
  20. Gomaa W. H. AndFahmy A. A. (2013). A survey of text similarity approaches. International Journal of Computer Applications, 68(13), 13-18.
  21. Collobert R. , Weston J. , Bottou L E. , Karlen M, Kavukcuoglu K, and Kuksa P. (2011). Natural language processing (almost) from scratch. The Journal of Machine Learning Research, 12:2493–2537, 2011.
  22. TREC Tracks, NIST http://trec. nist. gov/tracks. html visited Nov. 11, 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Automatic Multiple Choice Question Generation String Similarity Measures Semantic Role Labeling Named Entity Recognition.