%0 Journal Article %1 FerrucciBrownEtAl10aimag %A Ferrucci, David %A Brown, Eric %A Chu-Carroll, Jennifer %A Fan, James %A Gondek, David %A Kalyanpur, Aditya A. %A Lally, Adam %A Murdock, J. William %A Nyberg, Eric %A Prager, John %A Schlaefer, Nico %A Welty, Chris %D 2010 %J AI Magazine %K 01801 aaai paper ibm ai language processing information retrieval architecture answer zzz.iui %N 3 %P 59--79 %R 10.1609/aimag.v31i3.2303 %T Building Watson: An Overview of the DeepQA Project %V 31 %X IBM Research undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV Quiz show, Jeopardy! The extent of the challenge includes fielding a real-time automatic contestant on the show, not merely a laboratory exercise. The Jeopardy! Challenge helped us address requirements that led to the design of the DeepQA architecture and the implementation of Watson. After 3 years of intense research and development by a core team of about 20 researches, Watson is performing at human expert-levels in terms of precision, confidence and speed at the Jeopardy! Quiz show. Our results strongly suggest that DeepQA is an effective and extensible architecture that may be used as a foundation for combining, deploying, evaluating and advancing a wide range of algorithmic techniques to rapidly advance the field of QA.

@article{FerrucciBrownEtAl10aimag, abstract = {IBM Research undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV Quiz show, Jeopardy! The extent of the challenge includes fielding a real-time automatic contestant on the show, not merely a laboratory exercise. The Jeopardy! Challenge helped us address requirements that led to the design of the DeepQA architecture and the implementation of Watson. After 3 years of intense research and development by a core team of about 20 researches, Watson is performing at human expert-levels in terms of precision, confidence and speed at the Jeopardy! Quiz show. Our results strongly suggest that DeepQA is an effective and extensible architecture that may be used as a foundation for combining, deploying, evaluating and advancing a wide range of algorithmic techniques to rapidly advance the field of QA.}, added-at = {2016-03-29T15:15:11.000+0200}, author = {Ferrucci, David and Brown, Eric and Chu-Carroll, Jennifer and Fan, James and Gondek, David and Kalyanpur, Aditya A. and Lally, Adam and Murdock, J. William and Nyberg, Eric and Prager, John and Schlaefer, Nico and Welty, Chris}, biburl = {https://www.bibsonomy.org/bibtex/2460a61110ebf98c22be4669f6b8fe7fe/flint63}, doi = {10.1609/aimag.v31i3.2303}, file = {AAAI online:2010/FerrucciBrownEtAl10aimag.pdf:PDF}, groups = {public}, interhash = {c3fa1d7b2cfb8fc1742b8a4ec0151392}, intrahash = {460a61110ebf98c22be4669f6b8fe7fe}, issn = {0738-4602}, journal = {AI Magazine}, keywords = {01801 aaai paper ibm ai language processing information retrieval architecture answer zzz.iui}, number = 3, pages = {59--79}, timestamp = {2018-04-16T12:38:46.000+0200}, title = {Building {Watson}: An Overview of the {DeepQA} Project}, username = {flint63}, volume = 31, year = 2010 }