Natural Language Processing in Watson

Alfio Massimiliano Gliozzo, Aditya Kalyanpur, James Fan

Abstract: Open domain Question Answering (QA) is a long standing research problem. Recently, IBM took on this challenge in the context of the Jeopardy! game. Jeopardy! is a well-known TV quiz show that has been airing on television in the United States for more than 25 years. 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. The development of a system able to compete to grand champions in the Jeopardy! challenge led to the design of the DeepQA architecture and the implementation of Watson. The DeepQA project shapes a grand challenge in Computer Science that aims to illustrate how the wide and growing accessibility of natural language content and the integration and advancement of Natural Language Processing, Information Retrieval, Machine Learning, Knowledge Representation and Reasoning, and massively parallel computation can drive open-domain automatic Question Answering technology to a point where it clearly and consistently rivals the best human performance. Natural Language Processing (NLP) plays a crucial role in the overall Deep QA architecture. It allows to "make sense" of both question and unstructured knowledge contained in the large corpora where most of the answers are located. That's why we decided to focus this tutorial on the NLP technology adopted by Watson and on how it fits in the general Deep QA architecture.