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training data 0.0019380159999999999
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training set 0.001546314
data taggers 0.0015105420000000001
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correct pos 0.0012680459999999999
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few training 0.001246709
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tagging tasks 0.001123749
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different relevances 0.0010946719999999999
speech tagging 0.001055322
tagging phase 0.001039636
same rele 0.0010348380000000002
information gains 0.001029434
tagging disam 0.001028504
information gain 0.001014867
network models 0.00100526
information theory 9.91482E-4
input length 9.739550000000001E-4
training 9.64673E-4
overall performance 9.60845E-4
maximum length 9.60302E-4
output layer 9.507350000000001E-4
thai corpus 9.43918E-4
words 9.35698E-4
testing set 9.33537E-4
statistical methods 9.26369E-4
other languages 9.088169999999999E-4
actual output 9.06874E-4
output des 9.04112E-4
prior probability 9.03397E-4
output layers 9.01448E-4
correct rate 8.95331E-4
gram models 8.91946E-4
new entropy 8.910529999999999E-4
model 8.87997E-4
neuro models 8.84452E-4
basic taggers 8.73857E-4
neural network 8.70453E-4
learning 8.64589E-4
total number 8.579729999999999E-4
set informa 8.498210000000001E-4
neural networks 8.49493E-4
context priority 8.407E-4
fixed length 8.40028E-4
initial values 8.32597E-4
several kinds 8.25854E-4
previous result 8.17921E-4
neuro taggers 8.085290000000001E-4
other hand 8.08464E-4
suitable length 8.08041E-4
actual errors 8.02233E-4
vious taggers 7.991630000000001E-4
previous ection 7.94923E-4
initial weights 7.848989999999999E-4
lus ion 7.84154E-4
thai text 7.814199999999999E-4
tagging 7.72212E-4
input thai 7.70948E-4
ing sets 7.69919E-4
possible poss 7.687149999999999E-4
tion entropy 7.64922E-4
error 7.54564E-4
small thai 7.54086E-4
rmat ion 7.5164E-4
final result 7.49097E-4
neural net 7.48216E-4
