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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.
%0 Journal Article
%1 ferrucci10watson
%A ?,
%D 2010
%J AI Magazine
%K ibm nlp text-mining watson
%N 3
%P 59--79
%T Building Watson: An Overview of the DeepQA Project
%U http://www.aaai.org/ojs/index.php/aimagazine/article/view/2303
%V 31
%X AI MAGAZINE
Open Journal Systems
Journal Help
User
Username
Password
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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{ferrucci10watson,
abstract = {AI MAGAZINE
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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 = {2011-05-04T13:31:48.000+0200},
author = {?},
biburl = {https://www.bibsonomy.org/bibtex/24d8258d8a38a1e615d5346201b309d03/sb3000},
interhash = {9c001e1581c5b846f1690d4266f71bcb},
intrahash = {4d8258d8a38a1e615d5346201b309d03},
journal = {AI Magazine},
keywords = {ibm nlp text-mining watson},
number = 3,
pages = {59--79},
timestamp = {2011-05-04T13:31:48.000+0200},
title = {Building Watson: An Overview of the DeepQA Project},
url = {http://www.aaai.org/ojs/index.php/aimagazine/article/view/2303},
volume = 31,
year = 2010
}