Simulating IBM Watson in the Classroom

IBM Watson exemplifies multiple innovations in natural language processing and question answering. In addition, Watson uses most of the known techniques in these two domains as well as many methods from related domains. Hence, there is pedagogical value in a rigorous understanding of its function. The paper provides the description of a text analytics course focused on building a simulator of IBM Watson, conducted in Spring 2014 at UNC Charlotte. We believe this is the first time a simulation containing all the major Watson components was created in a university classroom. The system achieved a respectable (close to) 20% accuracy on Jeopardy! questions, and there remain many known and new avenues of improving performance that can be explored in the future. The code and documentation are available on GitHub. The paper is a joint effort of the teacher and some of the students who were leading teams implementing component technologies, and therefore deeply involved in making the class successful.