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joint model 0.001547005
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model merg 0.0013905810000000001
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feature counts 0.001343233
probability distribution 0.001333217
feature selection 0.0013150520000000001
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prior probability 0.0011525020000000001
markov models 0.001147627
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leak probability 0.001124075
other arc 0.0011183389999999999
natural language 0.001100621
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classic models 0.0010792759999999999
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parameter vector 0.0010700100000000001
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other ways 0.001049033
regexp language 0.001046621
data structure 0.001044627
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unsupervised training 9.97295E-4
incomplete data 9.72346E-4
path weight 9.720730000000001E-4
gem algorithm 9.71452E-4
hmm training 9.678929999999999E-4
input string 9.62998E-4
separate training 9.61555E-4
path probabilities 9.54912E-4
conditional case 9.54E-4
corresponding problem 9.46522E-4
output string 9.46218E-4
parameter estimation 9.46124E-4
exogenous training 9.45655E-4
criminative training 9.45655E-4
ing weight 9.36316E-4
conditional distribution 9.238779999999999E-4
same semiring 9.163909999999999E-4
probabilistic fst 9.130779999999999E-4
regular set 9.12713E-4
probability 9.10547E-4
random path 8.98189E-4
machine translation 8.92779E-4
many paths 8.909969999999999E-4
only input 8.84054E-4
conditional fst 8.76595E-4
weighted case 8.73785E-4
many parameters 8.707299999999999E-4
estimation method 8.69741E-4
models 8.66018E-4
parameter counts 8.65429E-4
state fst 8.63143E-4
state sequence 8.523669999999999E-4
path values 8.482850000000001E-4
first pruning 8.45316E-4
many operations 8.44815E-4
general approach 8.41418E-4
linear number 8.38786E-4
input suffixes 8.38126E-4
linear system 8.36175E-4
complex parameter 8.33275E-4
input symbol 8.32665E-4
exact input 8.30419E-4
final state 8.29669E-4
vector operations 8.29031E-4
joint case 8.21977E-4
language 8.19684E-4
