Abstract
This paper presents an eclectic approach for compressing weighted finite-state automata and transducers, with minimal impact on performance. The approach is eclectic in the sense that various complementary methods have been employed: row-indexed storage of sparse matrices, dictionary compression, bit manipulation, and lossless omission of data. The compression rate is over 83% with respect to the current Bell Labs finite-state library.
Cite
CITATION STYLE
Kiraz, G. A. (2001). Compressed storage of sparse finite-state transducers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2214, pp. 109–121). Springer Verlag. https://doi.org/10.1007/3-540-45526-4_11
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