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

Source: [1 Billion Pages = 1 Million Dollars? Mining the Web to Play "Who Wants to be a Millionaire?"](<https://www.sota2.com/research/paper/68fa60bb1e209131ce9f0d25>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T19:45:32.888Z

Authors: Shyong (Tony) K. Lam, David M Pennock, Dan Cosley, Steve Lawrence

Published: 2012-10-19T15:06:15.000Z

## 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 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1212.2477>)

[PDF](<https://arxiv.org/pdf/1212.2477.pdf>)