1 Billion Pages = 1 Million Dollars? mining the web to play "who wants to be a millionaire?"

  • Authors:

  • Shyong K. Lam;David M. Pennock;Dan Cosley;Steve Lawrence

  • Affiliations:

  • Computer Science Dept., University of Minnesota, Minneapolis, MN;Overture Services, Inc., Pasadena, CA;Computer Science Dept., University of Minnesota, Minneapolis, MN;NEC Laboratories America, Princeton, NJ

  • Venue:
  • UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
  • Year:
  • 2002

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Abstract

We exploit the redundancy and volume of information on the web to build a computerized player for the ABC TV game show "Who Wants To Be A Millionaire?". The player consists of a question-answering module and a decision-making module. The question-answering module utilizes question transformation techniques, natural language parsing, multiple information retrieval algorithms, and multiple search engines; results are combined in the spirit of ensemble learning using an adaptive weighting scheme. Empirically, the system correctly answers about 75% of questions from the Millionaire CD-ROM, 3rd edition--general-interest trivia questions often about popular culture and common knowledge. The decision-making module chooses from allowable actions in the game in order to maximize expected risk-adjusted winnings, where the estimated probability of answering correctly is a function of past performance and confidence in correctly answering the current question. When given a six question head start (i.e., when starting from the $2,000 level), we find that the system performs about as well on average as humans starting at the beginning. Our system demonstrates the potential of simple but well-chosen techniques for mining answers from unstructured information such as the web.