############################################################################# ## 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 3 words, which begins "", "", "majordome"... This is a CLOSED-vocabulary model (OOVs eliminated from training data and are forbidden in test data) Good-Turing discounting was applied. 1-gram frequency of frequency : 1 2-gram frequency of frequency : 1 0 0 0 0 0 0 3-gram frequency of frequency : 1 0 0 0 0 0 0 1-gram discounting ratios : 0.33 2-gram discounting ratios : 3-gram discounting ratios : 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. Data formats: Beginning of data mark: \data\ ngram 1=nr # number of 1-grams ngram 2=nr # number of 2-grams ngram 3=nr # number of 3-grams \1-grams: p_1 wd_1 bo_wt_1 \2-grams: p_2 wd_1 wd_2 bo_wt_2 \3-grams: p_3 wd_1 wd_2 wd_3 end of data mark: \end\ \data\ ngram 1=3 ngram 2=1 ngram 3=1 \1-grams: -0.4771 0.0000 -0.4771 -0.3010 -0.4771 majordome 0.0000 \2-grams: -0.1761 majordome -0.1249 \3-grams: -0.3010 majordome \end\