Natural Language Processing With Prolog in the IBM Watson System
Adam Lally, Paul Fodor · 2011
complex natural language questions over an extremely broad domain of knowledge. Moreover, it had to compute an accurate confidence in its answers and to complete its processing in a very short amount of time. The Question-Answering (QA) problem requires a machine to go beyond just match-ing keywords in documents, which is what a web-search engine does, and correctly in-terpret the question to figure out what is being asked. The QA system also needs to find the precise answer without requiring the aid of a human to read through the returned documents. To address these challenges, the research team at IBM developed a software archi-tecture called DeepQA, on which Watson is implemented. The DeepQA architecture assumes and pursues multiple interpretations of the question, generates many plausi-ble answers or hypotheses, collects evidence for these hypotheses, and evaluates the evidence to determine if it supports or refutes those hypotheses [2]. Watson contains hundreds of different algorithms that evaluate evidence along different dimensions. Watson utilizes Natural Language Processing (NLP) technology to interpret the