Abstract

The ADtree, a data structure useful for caching sufficient statistics, has been successfully adapted to grow lazily when memory is limited and to update sequentially with an incrementally updated dataset. However, even these modified forms of the ADtree still exhibit inefficiencies in terms of both space usage and query time, particularly on datasets with very high dimensionality and with high arity features. We propose five modifications to the ADtree, each of which can be used to improve size and query time under specific types of datasets and features. These modifications also provide an increased ability to precisely control how an ADtree is built and to tune its size given external memory or speed requirements.

Degree

MS

College and Department

Physical and Mathematical Sciences; Computer Science

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2008-07-10

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd2487

Keywords

ADtree, NLP, high arity, Penn Treebank, data structure, caching sufficient statistcs

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