We study methods of efficiently leveraging massive textual corpora through n-gram statistics.Specifically, we explore algorithms that use a database of frequency counts for sequences of tokens in a terawordWeb corpus to correct spelling mistakes and to extract a list of instances of some category given only the name of the target category. For spelling correction, we use a novel correction algorithm and demonstrate high accuracy in correcting both real-word errors and non-word errors. For category extraction, we show promising preliminary results for a variety of categories. We conclude that n-gram statistics provide an efficient way to use information contained in a massive corpus of text using relatively simple algorithms. The report ends with a reflection on problems encountered, possible solutions, and future work.

31 pages

*Institute for Software Research, Carnegie Mellon University

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