Watson, named after IBM founder Thomas J. Watson, was built by a team of IBM researchers who set out to accomplish a grand challenge-build a computing system that rivals a human's ability to answer questions posed in natural language with speed, accuracy and confidence. The quiz show Jeopardy! provided the ultimate test of this technology because the game's clues involve analyzing subtle meaning, irony, riddles and other complexities of natural language in which humans excel and computers traditionally fail. Watson passed its first test on Jeopardy!, beating the show's two greatest champions in a televised exhibition match, but the real test will be in applying the underlying natural language processing and analytics technology in business and across industries. In this talk I will introduce the Jeopardy! grand challenge, present an overview of Watson and the DeepQA technology upon which Watson is built, and explore future applications of this technology.

We describe a system for rapidly determining document similarity among a set of documents obtained from an information retrieval (IR) system. We obtain a ranked list of the most important terms in each document using a rapid phrase recognizer system. We store these in a database and compute document similarity using a simple database query. If the number of terms found to not be contained in both documents is less than some predetermined threshold compared to the total number of terms in the document, these documents are determined to be very similar. We compare this to the shingles approach.

Speech is a tantalizing mode of human communication. On the one hand, humans understand speech with ease and use speech to express complex ideas, information, and knowledge. On the other hand, automatic speech recognition with computers is still very hard, and extracting knowledge from speech is even harder. In this paper we motivate the study of speech as a knowledge resource and briefly survey a family of related applications and systems being developed at IBM Research aimed towards the goal of exploiting speech as a knowledge resource.

We present a new technique for question answering called Predictive Annotation. Predictive Annotation identifies potential answers to questions in text, annotates them accordingly and indexes them. This technique, along with a complementary analysis of questions, passage-level ranking and answer selection, produces a system effective at answering natural-language fact-seeking questions posed against large document collections. Experimental results show the effects of different parameter settings and lead to a number of general observations about the question-answering problem.

Information retrieval systems are being challenged to manage larger and larger document collections. In an effort to provide better retrieval performance on large collections, more sophisticated retrieval techniques have been developed that support rich, structured queries. Structured queries are not amenable to previously proposed optimization techniques. Optimizing execution, however, is even more important in the context of large document collections. We present a new structured query optimization technique which we have implemented in an inference network-based information retrieval system. Experimental results show that query evaluation time can be reduced by more than half with little impact on retrieval effectiveness.