Aggregation improves learning: Experiments in natural language generation for intelligent tutoring systems

ACL-05 - 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

Volumn , Issue , 2005, Pages 50-57

  Di Eugenio, Barbara ^a   Fossati, Davide ^a   Yu, Dan ^a   Haller, Susan ^b   Glass, Michael ^c  

^a UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

^b UNIVERSITY OF WISCONSIN PARKSIDE   (United States)

^c VALPARAISO UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

INTELLIGENT TUTORING SYSTEM; NATURAL LANGUAGE GENERATION; NATURAL LANGUAGE GENERATORS; ORIGINAL SYSTEMS;

COMPUTATIONAL LINGUISTICS; EDUCATION COMPUTING; NATURAL LANGUAGE PROCESSING SYSTEMS;

COMPUTER AIDED INSTRUCTION;

EID: 84859914462     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Giuseppe Carenini and Johanna D. Moore. 2000. An empirical study of the influence of argument conciseness on argument effectiveness. In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics, Hong Kong.
2. José Coch. 1996. Evaluating and comparing three textproduction techniques. In COLING96, Proceedings of the Sixteenth International Conference on Computational Linguistics, pages 249-254
3. Hercules Dalianis. 1996. Concise Natural Language Generation from Formal Specifications. Ph.D. thesis, Department of Computer and Systems Science, Stocholm University. Technical Report 96-008.
4. Barbara Di Eugenio, Michael Glass, and Michael J. Trolio. 2002. The DIAG experiments: Natural Language Generation for Intelligent Tutoring Systems. In INLG02, The Third International Natural Language Generation Conference, pages 120-127.
5. Barbara Di Eugenio, Davide Fossati, Dan Yu, Susan Haller, and Michael Glass. 2005. Natural language generation for intelligent tutoring systems: a case study. In AIED 2005, the 12th International Conference on Artificial Intelligence in Education.
6. M. W. Evens, J. Spitkovsky, P. Boyle, J. A. Michael, and A. A. Rovick. 1993. Synthesizing tutorial dialogues. In Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society, pages 137-140.
7. Michael Glass, Heena Raval, Barbara Di Eugenio, and Maarika Traat. 2002. The DIAG-NLP dialogues: coding manual. Technical Report UIC-CS 02-03, University of Illinois - Chicago.
8. A.C. Graesser, N. Person, Z. Lu, M.G. Jeon, and B. Mc- Daniel. 2005. Learning while holding a conversation with a computer. In L. PytlikZillig, M. Bodvarsson, and R. Brunin, editors, Technology-based education: Bringing researchers and practitioners together. Information Age Publishing.
9. Terrence Harvey and Sandra Carberry. 1998. Integrating text plans for conciseness and coherence. In ACL/COLING 98, Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics, pages 512-518.
10. Helmut Horacek. 2002. Aggregation with strong regularities and alternatives. In International Conference on Natural Language Generation.
11. Xiaoron Huang and Armin Fiedler. 1996. Paraphrasing and aggregating argumentative text using text structure. In Proceedings of the 8th InternationalWorkshop on Natural Language Generation, pages 21-30.
12. Rodger Kibble and Richard Power. 2000. Nominal generation in GNOME and ICONOCLAST. Technical report, Information Technology Research Institute, University of Brighton, Brighton, UK.
13. Benoît Lavoie and Owen Rambow. 1997. A fast and portable realizer for text generation systems. In Proceedings of the Fifth Conference on Applied Natural Language Processing.
14. James C. Lester and Bruce W. Porter. 1997. Developing and empirically evaluating robust explanation generators: the KNIGHT experiments. Computational Linguistics, 23(1):65-102.
15. D. J. Litman, C. P. Rosé, K. Forbes-Riley, K. VanLehn, D. Bhembe, and S. Silliman. 2004. Spoken versus typed human and computer dialogue tutoring. In Proceedings of the Seventh International Conference on Intelligent Tutoring Systems, Maceio, Brazil.
16. Mike Reape and Chris Mellish. 1998. Just what is aggregation anyway? In Proceedings of the European Workshop on Natural Language Generation.
17. Ehud Reiter and Robert Dale. 2000. Building Natural Language Generation Systems. Studies in Natural Language Processing. Cambridge University Press.
18. Ehud Reiter, Roma Robertson, and Liesl Osman. 2003. Lessons from a failure: Generating tailored smoking cessation letters. Artificial Intelligence, 144:41-58.
19. S. C. Shapiro. 2000. SNePS: A logic for natural language understanding and commonsense reasoning. In L. M. Iwanska and S. C. Shapiro, editors, Natural Language Processing and Knowledge Representation. AAAI Press/MIT Press.
20. James Shaw. 2002. A corpus-based analysis for the ordering of clause aggregation operators. In COLING02, Proceedings of the 19th International Conference on Computational Linguistics.
21. Douglas M. Towne. 1997. Approximate reasoning techniques for intelligent diagnostic instruction. International Journal of Artificial Intelligence in Education.
22. Michael White and Ted Caldwell. 1998. Exemplars: A practical, extensible framework for dynamic text generation. In Proceedings of the Ninth International Workshop on Natural Language Generation, pages 266-275, Niagara-on-the-Lake, Canada.
23. Ching-Long Yeh and Chris Mellish. 1997. An empirical study on the generation of anaphora in Chinese. Computational Linguistics, 23(1):169-190. (Pubitemid 127458661)
24. R. Michael Young. 1999. Using Grice's maxim of quantity to select the content of plan descriptions. Artificial Intelligence, 115:215-256. (Pubitemid 30545514)