A framework for merging and ranking of answers in DeepQA

IBM Journal of Research and Development

Volumn 56, Issue 3-4, 2012, Pages

  Gondek, D C ^a   Lally, A ^a   Kalyanpur, A ^a   Murdock, J W ^a   Duboue, P A ^b   Zhang, L ^c   Pan, Y ^c   Qiu, Z M ^c   Welty, C ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

^b Les Laboratoires Foulab   (Canada)

^c IBM RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AGILE DEVELOPMENT ENVIRONMENTS; BASELINE MACHINES; ERROR PRONES; HYPOTHESIS EVALUATION; LEARNING APPROACH; LEARNING FRAMEWORKS;

COMPUTER SCIENCE; RESEARCH;

LEARNING SYSTEMS;

EID: 84859868588     PISSN: 00188646     EISSN: 21518556     Source Type: Journal    
DOI: 10.1147/JRD.2012.2188760     Document Type: Review

References (19)

1. J. Chu-Carroll, J. Fan,B. K. Boguraev, D. Carmel, D. Sheinwald, and C. Welty, "Finding needles in the haystack: Search and candidate generation," IBM J. Res. & Dev., vol. 56, no. 3/4, Paper 6, pp. 6:1-6:12, May/Jul. 2012.
2. D. A. Ferrucci, "Introduction to FThis is Watson," IBM J. Res. & Dev., vol. 56, no. 3/4, Paper 1, pp. 1:1-1:15, May/Jul. 2012.
3. A. Lally, J. M. Prager, M. C. McCord,B. K. Boguraev, S. Patwardhan, J. Fan, P. Fodor, and J. Chu-Carroll, "Question analysis: How Watson reads a clue," IBM J. Res. & Dev., vol. 56, no. 3/4, Paper 2, pp. 2:1-2:14, May/Jul. 2012.
4. D. H. Wolpert, "Stacked generalization," Neural Netw., vol. 5, no. 2, pp. 241-259, 1992.
5. D. Ghosh, DSLs in Action. Greenwich, CT: Manning Publ., 2010.
6. J. Ko, E. Nyberg, and L. Si, "A probabilistic graphical model for joint answer ranking in question answering," in Proc. 30th Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2007, pp. 343-350. (Pubitemid 350164979)
7. J. W. Murdock, J. Fan, A. Lally, H. Shima, and B. K. Boguraev, "Textual evidence gathering and analysis," IBM J. Res. & Dev., vol. 56, no. 3/4, Paper 8, pp. 8:1-8:14, May/Jul. 2012.
8. M. Balcan, N. Bansal, A. Beygelzimer, D. Coppersmith, J. Langford, and G. B. Sorkin, "Robust reductions from ranking to classification," in Proc. Mach. Learn., 2008, pp. 139-153.
9. R. Herbrich, T. Graepel, and K. Obermayer, "Large margin rank boundaries for ordinal regression," Adv. Large Margin Classifiers, vol. 88, no. 2, pp. 115-132, 2000.
10. Y. Cao, J. Xu, T. Y. Liu, H. Li, Y. Huang, and H. W. Hon, "Adapting ranking SVM to document retrieval," in Proc. 29th Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2006, pp. 186-193.
11. C. G. Atkeson, A. W. Moore, and S. Schaal, "Locally weighted learning," Artif. Intell. Rev., vol. 11, no. 1-5, pp. 11-73, Feb. 1997. (Pubitemid 127508233)
12. J. W. Murdock, A. Kalyanpur, C. Welty, J. Fan, D. A. Ferrucci, D. C. Gondek, L. Zhang, and H. Kanayama, "Typing candidate answers using type coercion," IBM J. Res. & Dev., vol. 56, no. 3/4, Paper 7, pp. 7:1-7:13, May/Jul. 2012.
13. R. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton, "Adaptive mixtures of local experts," Neural Comput., vol. 3, no. 1, pp. 79-87, Spring 1991.
14. H. Liu and R. Setiono, "A probabilistic approach to feature selectionVA filter solution," in Proc. 13th ICML, "ari, Italy, 1996, pp. 319-327.
15. Mach. Learn. Group, Univ. Waikato, Weka 3: Data Mining Software in Java. [Online]. Available: http://www.cs.waikato. ac.nz/ml/weka/.
16. C. Bizer, J. Lehmann, G. Kobilarov, S. Auer, C. Becker, R. Cyganiak, and S. Hellmann, "DBpediaVA crystallization point for the web of data," J. Web Semantics, Sci., Serv. Agents World Wide Web, vol. 7, no. 3, pp. 154-165, Sep. 2009.
17. A. Kalyanpur,B. K. Boguraev, S. Patwardhan, J. W. Murdock, A. Lally, C. Welty, J. M. Prager,B. Coppola, A. Fokoue-Nkoutche, L. Zhang, Y. Pan, and Z. M. Qiu, "Structured data and inference in DeepQA," IBM J. Res. & Dev., vol. 56, no. 3/4, Paper 10, pp. 10:1-10:14, May/Jul. 2012.
18. J. Fan, A. Kalyanpur, D. C. Gondek, and D. A. Ferrucci, "Automatic knowledge extraction from documents," IBM J. Res. & Dev., vol. 56, no. 3/4, Paper 5, pp. 5:1-5:10, May/Jul. 2012.
19. E. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2nd ed. New York: Springer-Verlag, 2008. [Online]. Available: http://www-stat.stanford.edu/̃tibs/ElemStatLearn/.