International Journal of Computer and Information Technology

Open Access Open Access  Restricted Access Subscription Access

An Intelligent Approach to Automatic Query Formation from Plain Text Using Artificial Intelligence


Affiliations
1 Dept. Computer Science & Engg., Integral University, Lucknow, India
2 Information Technology Department, University of Technology and Applied Sciences-CAS Ibri, Oman
 

Man have always been, inherently, curious creatures. They ask questions in order to satiate their insatiable curiosity. For example, kids ask questions to learn more from their teachers, teachers ask questions to assist themselves to evaluate student performance, and we all ask questions in our daily lives. Numerous learning exchanges, ranging from one-on-one tutoring sessions to thorough exams, as well as real-life debates, rely heavily on questions. One notable fact is that, due to their inconsistency in particular contexts, humans are often inept at asking appropriate questions. It has been discovered that most people have difficulty identifying their own knowledge gaps. This becomes our primary motivator for automating question generation in the hopes that the benefits of an automated Question Generation (QG) system will help humans achieve their useful inquiry needs. QG and Information Extraction (IE) have become two major issues for language processing communities, and QG has recently become an important component of learning environments, systems, and information seeking systems, among other applications. The Text-to-Question generation job has piqued the interest of the Natural Language Processing (NLP), Natural Language Generation (NLG), Intelligent Tutoring System (ITS), and Information Retrieval (IR) groups as a possible option for the shared task. A text is submitted to a QG system in the Text-to-Question generation task. Its purpose would be to create a series of questions for which the text has answers (such as a word, a set of words, a single sentence, a text, a set of texts, a stretch of conversational dialogue, an inadequate query, and so on).

Keywords

Automatic Question Generation, NLP, Intelligent Tutoring System (ITS), IR, Query Processing.
User
Notifications
Font Size

  • Karen Mazidi, Rodney D. Nielsen, "Linguistic Considerations in Automatic Question Generation", Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Short Papers), pages 321–326, Baltimore, Maryland, USA, June 23-25 2014 “ Automatic Question Genaration Asyntactical Approach” Husam Deeb Abdullah Deeb Ali, 2012

  • Manish Agarwal and Prashanth Mannem, "Automatic Gap-fill Question Generation from Text Books", Proceedings of the Sixth Workshop on Innovative Use of NLP for Building Educational Applications, pages 56–64, Portland, Oregon, 24 June 2011.

  • “Using Questions to Teach Better”Rick Garlikov-http://www.garlikov.com/teaching/usingquestions.html, 2010

  • D. Metzler and W. B. Croft, “Analysis of statistical question classification for fact-based questions,” Inf. Retr., vol. 8, no. 3, pp. 481–504, May2005.

  • Jonathan C. Brown, Gwen A. Frishkoff, Maxine Eskenazi "Automatic Question Generation for Vocabulary Assessment", Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 819–826, Vancouver, October 2005

  • M. Agarwal, “Cloze and open cloze question generation systems and their evaluation guidelines,” Journal of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 819–826, Vancouver, October 2005

  • Goto, T. Kojiri, T. Watanabe, T. Iwata, and T. Yamada, “An automatic generation of multiple-choice cloze questions based on statistical learning,” in proceedings of the 17th International Conference on Computers in Education. Asia-Pacific Society for Computers in Education, 2009, pp.415–222.

  • J. C. Brown, G. A. Frishkoff, and M. Eskenazi, “Automatic ques- tion generation for vocabulary assessment,” inroceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, ser.

  • HLT ’05. Stroudsburg, PA, USA: Association for Computational Linguistics, 2005, pp.819–826.

  • C.-Y. Chen, H.-C. Liou, and J. S. Chang, “Fast: An automatic generation system for grammar tests,” in Proceedings of the COL- ING/ACL on Interactive Presentation Sessions, ser. COLING-ACL ’06. Stroudsburg, PA, USA: Association for Computational Linguistics, 2006, pp.1–4.

  • Manish Agarwal and Prashanth Mannem, "Automatic Gap-fill Question Generation from Text Books", Proceedings of the Sixth Workshop on Innovative Use of NLP for Building Educational Applications, pages 56–64, Portland, Oregon, 24 June 2011.

  • I. S. Jacobs and C. P. Bean, “Generating question from text using context analysis,” in International Journal of Computer Science & Information Reasearch Advancement, vol. III, M. Akbar,. A.K Shaunl, Eds. Boasten: Academic, 2018, pp. 312–323.

  • Alessi, S. M., Anderson, R. C., Anderson T. H., Biddle, W. B., Dalgaard, B. R., Paden, D. W., Smock, H. R., Surber, J. R., & Wietecha, E. J. Development and implementation of the computer assisted instruction study management system (CAISMS). San Diego, California: Navy Personnel Research and Development Center, Technical Report TR 74-29, February 1974.

  • Anderson, T. H., Anderson, R. C., Alessi, S. M., Dalgaard, B. R., Paden, D. W., Biddle, W. B., Surber, J. R., & Smock, H. R. A multifaceted computer-based course management system. San Diego, California: Navy Personnel Research and Development Center, Technical Report TR 75-30, April 1975.

  • Anderson, T. H., Anderson, R. C., Dalgaard, B. R., Paden, D. W., Biddle, W. R., Surber, J. R., & Alessi, S. R. An experimental evaluation of a computer-assisted instruction study management system (CAISMS). San Diego, California: Navy Personnel Research and Development Center, Technical Report TR 75-31, April.

  • 197“Automatic Question Generation using Discourse Cues and Distractor Selection for Cloze Questions” Rakshit Shah Language Technology and Research Center (LTRC)

  • Isha Sood, 7 Characteristics Of A Good Question. Online available at:https://elearningindustry.com/characteristics-of-a-good-question-7

  • "Automatic Factual Question Generation from Text" Michael Heilman CMU-LTI-11-004

  • “Towards Automatic Topical Question Generation” Y llias ,Chali Sadid , A.Hasan University of Lethbridge, Lethbridge, AB, Canada, 2011

  • https://www.statpac.com/surveys/question- qualities.htm

  • Ming Liu, R. A. Calvo, A. Aditomo, L.A. Pizzato, “Using Wikipedia and Conceptual Graph Structures to Generate Questions for Academic Writing Support” Third Quarter 2012, pp. 251-263, vol. 5 DOI Bookmark: 10.1109/TLT.2012.5

  • T. Tokunaga, H. Nishikawa, and H. Obari, “Evalu- ation of automatically generated english vocabulary questions,” Research and PracticeinTechnologyEnhancedLearning,vol.12,no.1,p.11,2017.

  • R. Mitkov, L. An Ha, and N. Karamanis, “A computer-aided environment for generating multiple-choice test items,” Nat. Lang. Eng., vol. 12, no. 2, pp. 177–194, Jun.2006.

  • M. Liu, V. Rus, and L. Liu, “Automatic chinese factual question generation,” IEEE Transactions on Learning Technologies, vol. 10, no. 2, pp. 194–204,2017.

  • D. Metzler and W. B. Croft, “Analysis of statistical question classification for fact-based questions,” Inf. Retr., vol. 8, no. 3, pp. 481–504, May2005.

  • M. Agarwal, “Cloze and open cloze question generation systems and their evaluation guidelines,” International Institute of Informa- tion Technology, Hyderabad,2012.

  • T. Goto, T. Kojiri, T. Watanabe, T. Iwata, and T. Yamada, “An automatic generation of multiple-choice cloze questions based on statistical leang,” in roceedings of the 17th International Conference on Computers in Education. Asia-Pacific Society for Computers in Education, 2009, pp.415–222.

  • J. C. Brown, G. A. Frishkoff, and M. Eskenazi, “Automatic ques- tion generation for vocabulary assessment,” in Proceedings of the ConferenceonHumanLanguageTechnologyandEmpiricalMethod s in Natural Language Processing, ser. HLT ’05. Stroudsburg, PA, USA: Association for Computational Linguistics, 2005, pp.819–826.

  • C.-Y. Chen, H.-C. Liou, and J. S. Chang, “Fast: An automatic generation system for grammar tests,” in Proceedings of the

  • COL- ING/ACL on Interactive Presentation Sessions, ser. COLING-ACL ’06. Stroudsburg, PA, USA: Association for Computational Linguistics, 2006, pp.1–4.


Abstract Views: 165

PDF Views: 110




  • An Intelligent Approach to Automatic Query Formation from Plain Text Using Artificial Intelligence

Abstract Views: 165  |  PDF Views: 110

Authors

Mohd Akbar
Dept. Computer Science & Engg., Integral University, Lucknow, India
Mohd Shahid Hussain
Information Technology Department, University of Technology and Applied Sciences-CAS Ibri, Oman
Mohd Suaib
Dept. Computer Science & Engg., Integral University, Lucknow, India

Abstract


Man have always been, inherently, curious creatures. They ask questions in order to satiate their insatiable curiosity. For example, kids ask questions to learn more from their teachers, teachers ask questions to assist themselves to evaluate student performance, and we all ask questions in our daily lives. Numerous learning exchanges, ranging from one-on-one tutoring sessions to thorough exams, as well as real-life debates, rely heavily on questions. One notable fact is that, due to their inconsistency in particular contexts, humans are often inept at asking appropriate questions. It has been discovered that most people have difficulty identifying their own knowledge gaps. This becomes our primary motivator for automating question generation in the hopes that the benefits of an automated Question Generation (QG) system will help humans achieve their useful inquiry needs. QG and Information Extraction (IE) have become two major issues for language processing communities, and QG has recently become an important component of learning environments, systems, and information seeking systems, among other applications. The Text-to-Question generation job has piqued the interest of the Natural Language Processing (NLP), Natural Language Generation (NLG), Intelligent Tutoring System (ITS), and Information Retrieval (IR) groups as a possible option for the shared task. A text is submitted to a QG system in the Text-to-Question generation task. Its purpose would be to create a series of questions for which the text has answers (such as a word, a set of words, a single sentence, a text, a set of texts, a stretch of conversational dialogue, an inadequate query, and so on).

Keywords


Automatic Question Generation, NLP, Intelligent Tutoring System (ITS), IR, Query Processing.

References