############################################################################# ## Copyright (c) 1996, Carnegie Mellon University, Cambridge University, ## Ronald Rosenfeld and Philip Clarkson ## Version 3, Copyright (c) 2006, Carnegie Mellon University ## Contributors includes Wen Xu, Ananlada Chotimongkol, ## David Huggins-Daines, Arthur Chan and Alan Black ############################################################################# ============================================================================= =============== This file was produced by the CMU-Cambridge =============== =============== Statistical Language Modeling Toolkit =============== ============================================================================= This is a 3-gram language model, based on a vocabulary of 11982 words, which begins "0", "000", "001"... This is an OPEN-vocabulary model (type 1) (OOVs were mapped to UNK, which is treated as any other vocabulary word) Good-Turing discounting was applied. 1-gram frequency of frequency : 6201 2-gram frequency of frequency : 47164 5609 1840 1011 565 400 260 3-gram frequency of frequency : 80736 4153 1013 506 269 183 101 1-gram discounting ratios : 1.00 2-gram discounting ratios : 0.21 0.47 0.72 0.69 0.84 0.75 0.84 3-gram discounting ratios : 0.10 0.36 0.66 0.66 0.81 0.64 0.89 This file is in the ARPA-standard format introduced by Doug Paul. p(wd3|wd1,wd2)= if(trigram exists) p_3(wd1,wd2,wd3) else if(bigram w1,w2 exists) bo_wt_2(w1,w2)*p(wd3|wd2) else p(wd3|w2) p(wd2|wd1)= if(bigram exists) p_2(wd1,wd2) else bo_wt_1(wd1)*p_1(wd2) All probs and back-off weights (bo_wt) are given in log10 form. 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