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We exploit the redundancy and volume of information on the web to build acomputerized player for the ABC TV game show 'Who Wants To Be A Millionaire?'The player consists of a question-answering module and a decision-makingmodule. The question-answering module utilizes question transformationtechniques, natural language parsing, multiple information retrievalalgorithms, and multiple search engines; results are combined in the spirit ofensemble learning using an adaptive weighting scheme. Empirically, the systemcorrectly answers about 75% of questions from the Millionaire CD-ROM, 3rdedition - general-interest trivia questions often about popular culture andcommon knowledge. The decision-making module chooses from allowable actions inthe game in order to maximize expected risk-adjusted winnings, where theestimated probability of answering correctly is a function of past performanceand confidence in in correctly answering the current question. When given a sixquestion head start (i.e., when starting from the $2,000 level), we find thatthe system performs about as well on average as humans starting at thebeginning. Our system demonstrates the potential of simple but well-chosentechniques for mining answers from unstructured information such as the web.

1 Billion Pages = 1 Million Dollars? Mining the Web to Play "Who Wants to be a Millionaire?"