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• W1869650270 abstract "For compression of text databases, semi-static word based models are a pragmatic choice. Previous experiments have shown that, where there is not sufficient memory to store a full word based model, encoding rare words as sequences of characters can still allow good compression, while a pure character based model is poor. We propose a further kind of model that reduces main memory costs: approximate models, in which rare words are represented by similarly spelt common words and a sequence of edits. We investigate the compression available with different models, including characters, words, word pairs, and edits, and with combinations of these approaches. We show experimentally that carefully chosen combinations of models can improve the compression available in limited memory and greatly reduce overall memory requirements." @default.
• W1869650270 created "2016-06-24" @default.
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• W1869650270 date "2003-01-20" @default.
• W1869650270 modified "2023-10-05" @default.
• W1869650270 title "Compact in-memory models for compression of large text databases" @default.
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• W1869650270 doi "https://doi.org/10.1109/spire.1999.796599" @default.
• W1869650270 hasPublicationYear "2003" @default.
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