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David A. Ferrucci
Researcher at IBM
Publications - 79
Citations - 7996
David A. Ferrucci is an academic researcher from IBM. The author has contributed to research in topics: Question answering & Set (abstract data type). The author has an hindex of 40, co-authored 77 publications receiving 7697 citations.
Papers
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Journal ArticleDOI
Building Watson: An Overview of the DeepQA Project
David A. Ferrucci,Eric W. Brown,Jennifer Chu-Carroll,James Fan,David C. Gondek,Aditya Kalyanpur,Adam Lally,J. William Murdock,Eric Nyberg,John M. Prager,Nico Schlaefer,Chris Welty +11 more
TL;DR: The 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.
Journal ArticleDOI
UIMA: an architectural approach to unstructured information processing in the corporate research environment
David A. Ferrucci,Adam Lally +1 more
TL;DR: A general introduction to U IMA is given focusing on the design points of its analysis engine architecture and how UIMA is helping to accelerate research and technology transfer is discussed.
Journal ArticleDOI
Introduction to This is Watson
TL;DR: A brief history of the events and ideas that positioned the team to take on the Jeopardy! challenge, build Watson, IBM Watson™, and ultimately triumph is provided, and how the system performed at champion levels is summarized.
Patent
System and method for providing answers to questions
TL;DR: In this paper, a system, method and computer program product for providing answers to questions based on any corpus of data is presented, which facilitates generating a number of candidate passages from the corpus that answer an input query, and finds the correct resulting answer by collecting supporting evidence from the multiple passages.
Patent
Questions and answers generation
TL;DR: In this article, a system, method and/or computer program product for automatically generating questions and answers based on any corpus of data is presented, given a collection of textual documents, automatically generating collections of questions about the documents together with answers to those questions.