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Watson was created as a question answering (QA) computing system that IBM built to apply advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to the field of open domain question answering.
Challenge. To find and understand the clues in the questions, Watson compared possible answers by ranking its confidence in their accuracy, and responded — all in under three seconds. Watson sparked curiosity around “machines that could think” and opened up the possibilities around how AI could be applied to business.
Watson’s main innovation centered on its ability to quickly execute hundreds of algorithms to simultaneously analyze a question from many directions, find and score potential answers, gather additional supporting evidence for each answer, and evaluate everything using natural language processing.
Question answering. Question-answering tasks are useful in help systems and other scenarios where frequently asked or more nuanced questions can be answered from existing content. To help the model return factual answers, implement the retrieval-augmented generation pattern. For more information, see Retrieval-augmented generation.
Rob High. Learn how cognitive systems, such as IBM Watson, can transform how organizations think, act, and operate. Understand the natural language processing capabilities and more of IBM Watson. See how evidence-based responses can drive better outcomes. Executive overview.
Apr 3, 2012 · Abstract: The first stage of processing in the IBM Watson™ 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 analysis capabilities: a deep Slot Grammar parser, a named entity recognizer, a co-reference ...
builds on the IBM WatsonTM question answer-ing system that takes natural language questions as input and produces precise answers along with accurate confidences as output. Watson-Paths breaks down the input scenario into indi-vidual pieces of information, asks relevant sub-questions of Watson to conclude new informa-
