Rather than relying on any single method of linguistic or semantic analysis, or method of judging relevance of retrieved passages and textual “nuggets” therein, Watson applies multiple methods to the questions and candidate answers, including methods of question classification, focal entity detection, parsing, chunking, lexical analysis, logical form computation, referent determination, relation detection, temporal analysis, and special methods for question-answer pairs involving puns, anagrams, and other twists common in Jeopardy!. Different question analyses are used separately to retrieve relevant documents, and to derive, analyze and score potential answers from passages and sentences in those documents. In general, numerous candidate answers to a question are produced, and their analyses provide hundreds of features whose weights for obtaining ranked answers with corresponding confidence levels are learned by ML methods applied to a corpus of past Jeopardy! questions and answers (or officially, answers and questions, according to the peculiar conceit of the Jeopardy! protocol). Watson's wagers are based on the confidence levels of its potential answers and a complex regression model.