Brilliant paper on The AI Behind Watson; Abstract here:
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).
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).
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