Scalable Text Mining with Sparse Generative Models
Table 10: Modification affixes and reference pages in the thesis
['affix', 'modification', 'reference pages']
The experiments compare a number of models across datasets and measures. Modifications to models are denoted by acronym affixes separated by underscores, for example “jm” for Jelinek-Mercer smoothed models, and “u_dp” for uniform-background Dirichlet prior smoothed models. Throughout the experiments only a subset of possible modifications is attempted, because there are 39 combinations for the basic smoothing methods, and far more when feature weighting and structural models are included. None of the MNB or TDM modifications increase the time or space complexities of estimation or inference, or introduce considerable additional constants to processing requirements. IDF lifting can decrease the complexities, sparsifying documents further by weighting frequently occurring words to 0.