ADtrees, n-gram count, corpora data
We consider the problem of efficiently storing n-gram counts for large n over very large corpora. In such cases, the efficient storage of sufficient statistics can have a dramatic impact on system performance. One popular model for storing such data derived from tabular data sets with many attributes is the ADtree. Here, we adapt the ADtree to benefit from the sequential structure of corpora-type data. We demonstrate the usefulness of our approach on a portion of the well-known Wall Street Journal corpus from the Penn Treebank and show that our approach is exponentially more efficient than the naïve approach to storing n-grams and is also significantly more efficient than a traditional prefix tree.
Original Publication Citation
Rob Van Dam and Dan Ventura, "ADtrees for Sequential Data and N-gram Counting", Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 492-497, 27.
BYU ScholarsArchive Citation
Van Dam, Robert and Ventura, Dan A., "ADtrees for Sequential Data and N-gram Counting" (2007). All Faculty Publications. 943.
Physical and Mathematical Sciences
© 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Copyright Use Information