Building Watson: An Overview of the DeepQA Project
Ferrucci, David, Brown, Eric, Chu-Carroll, Jennifer, Fan, James, Gondek, David, Kalyanpur, Aditya A., Lally, Adam, Murdock, J. William, Nyberg, Eric, Prager, John, Schlaefer, Nico, Welty, Chris, AI Magazine
The goals of IBM Research are to advance computer science by exploring new ways for computer technology to affect science, business, and society. Roughly three years ago, IBM Research was looking for a major research challenge to rival the scientific and popular interest of Deep Blue, the computer chess-playing champion (Hsu 2002), that also would have clear relevance to IBM business interests.
With a wealth of enterprise-critical information being captured in natural language documentation of all forms, the problems with perusing only the top 10 or 20 most popular documents containing the user's two or three key words are becoming increasingly apparent. This is especially the case in the enterprise where popularity is not as important an indicator of relevance and where recall can be as critical as precision. There is growing interest to have enterprise computer systems deeply analyze the breadth of relevant content to more precisely answer and justify answers to user's natural language questions. We believe advances in question-answering (QA) technology can help support professionals in critical and timely decision making in areas like compliance, health care, business integrity, business intelligence, knowledge discovery, enterprise knowledge management, security, and customer support. For researchers, the open-domain QA problem is attractive as it is one of the most challenging in the realm of computer science and artificial intelligence, requiring a synthesis of information retrieval, natural language processing, knowledge representation and reasoning, machine learning, and computer-human interfaces. It has had a long history (Simmons 1970) and saw rapid advancement spurred by system building, experimentation, and government funding in the past decade (Maybury 2004, Strzalkowski and Harabagiu 2006).
With QA in mind, we settled on a challenge to build a computer system, called Watson, (1) which 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.
Jeopardy! is a well-known TV quiz show that has been airing on television in the United States for more than 25 years (see the Jeopardy! Quiz Show sidebar for more information on the show). It pits three human contestants against one another in a competition that requires answering rich natural language questions over a very broad domain of topics, with penalties for wrong answers. The nature of the three-person competition is such that confidence, precision, and answering speed are of critical importance, with roughly 3 seconds to answer each question. A computer system that could compete at human champion levels at this game would need to produce exact answers to often complex natural language questions with high precision and speed and have a reliable confidence in its answers, such that it could answer roughly 70 percent of the questions asked with greater than 80 percent precision in 3 seconds or less.
Finally, the Jeopardy Challenge represents a unique and compelling AI question similar to the one underlying DeepBlue (Hsu 2002)--can a computer system be designed to compete against the best humans at a task thought to require high levels of human intelligence, and if so, what kind of technology, algorithms, and engineering is required? While we believe the Jeopardy Challenge is an extraordinarily demanding task that will greatly advance the field, we appreciate that this challenge alone does not address all aspects of QA and does not by any means close the book on the QA challenge the way that Deep Blue may have for playing chess.
The Jeopardy Challenge
Meeting the Jeopardy Challenge requires advancing and incorporating a variety of QA technologies including parsing, question classification, question decomposition, automatic source acquisition and evaluation, entity and relation detection, logical form generation, and knowledge representation and reasoning. …