Build a question answering system that can compete against humans in a “quiz bowl”-like game. Create tf-idf vector representations of questions and answer documents (wiki pages). Create a KNN classifier with vector representation of questions as inputs to give an answer. “Automatically Buzz” for answers above a certain threshold value even before the whole question end. For every answer, we get a confidence measure [which is the cosine similarity measure of the vector representations] of the Question and the Answer document. If the answer is over a threshold for any sentence in the question, buzz and make it the answer. If no answer is above the threshold, buzz the answer predicted from the last sentence.