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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(More)
Watson reads a clue A. Lally J. M. Prager M. C. McCord B. K. Boguraev S. Patwardhan J. Fan P. Fodor J. Chu-Carroll The first stage of processing in the IBM Watsoni system is to perform a detailed analysis of the question in order to determine what it is asking for and how best to approach answering it. Question analysis uses Watson’s parsing and semantic(More)
As part of the ongoing project, Project Halo, our goal is to build a system capable of answering questions posed by novice users to a formal knowledge base. In our current context, the knowledge base covers selected topics in physics, chemistry, and biology, and our question set consists of AP (advanced high-school) level examination questions. The task is(More)
Vulcan Inc.’s Project Halo is a multi-staged effort to create a Digital Aristotle, an application that will encompass much of the world's scientific knowledge and be capable of applying sophisticated problem-solving to answer novel questions. Vulcan envisions two primary roles for the Digital Aristotle: as a tutor to instruct students in the sciences, and(More)
extraction from documents J. Fan A. Kalyanpur D. C. Gondek D. A. Ferrucci Access to a large amount of knowledge is critical for success at answering open-domain questions for DeepQA systems such as IBM Watsoni. Formal representation of knowledge has the advantage of being easy to reason with, but acquisition of structured knowledge in open domains from(More)
Basic research in knowledge representation and reasoning (KR&R) has steadily advanced over the years, but it has been difficult to assess the capability of fielded systems derived from this research. In this paper, we present a knowledge-based question-answering system that we developed as part of a broader effort by Vulcan Inc. to assess KR&R technologies,(More)
haystack: Search and candidate generation J. Chu-Carroll J. Fan B. K. Boguraev D. Carmel D. Sheinwald C. Welty A key phase in the DeepQA architecture is Hypothesis Generation, in which candidate system responses are generated for downstream scoring and ranking. In the IBM Watsoni system, these hypotheses are potential answers to Jeopardy!i questions and are(More)
This paper describes a novel approach to the semantic relation detection problem. Instead of relying only on the training instances for a new relation, we leverage the knowledge learned from previously trained relation detectors. Specifically, we detect a new semantic relation by projecting the new relation’s training instances onto a lower dimension topic(More)