hindi word 0.003209369
input word 0.003196318
next word 0.0031832279999999998
unknown word 0.003151836
new word 0.003127196
single word 0.003111587
word level 0.003089744
ambiguous word 0.003056255
pos tag 0.00305281
inflected word 0.003037433
word groups 0.003023037
pos tagger 0.002937818
pos learning 0.0028650489999999997
different pos 0.00284857
pos tagging 0.002796057
pos tags 0.00276983
other pos 0.002689334
pos category 0.002548247
many pos 0.002470025
hindi pos 0.002463869
pos categories 0.002426977
appropriate pos 0.002373803
partial pos 0.002365956
greek pos 0.002313773
pos accuracies 0.002309284
individual pos 0.002304396
words accuracy 0.002296535
sible pos 0.002288431
rect pos 0.002287978
unknown words 0.001970386
new words 0.001945746
different feature 0.00188493
known words 0.001879494
ambiguous words 0.001874805
neighbouring words 0.001849306
ous words 0.001847221
vidual words 0.001842814
foreign words 0.001841979
training corpus 0.0017889
feature information 0.001775937
analysis rules 0.001592922
different tags 0.0015911800000000002
lexical tags 0.00158711
words 0.00157766
feature values 0.0015464909999999999
tagging task 0.001524408
morphological information 0.001498486
tagger table 0.001491906
disambiguation rules 0.001474142
possible tags 0.001467371
rule learning 0.001441908
correct tag 0.001435573
morphological analysis 0.001425802
hindi language 0.001410579
appropriate tag 0.001399393
morphology rules 0.0013784420000000001
tagger morphology 0.001363011
feature functions 0.0013616
training set 0.001360004
order language 0.001359557
resultant tag 0.0013458240000000002
frequent tag 0.001342055
training corpora 0.001331055
gnptam feature 0.0013222219999999999
different context 0.001316461
training cor 0.001314303
statistical learning 0.001313032
tag building 0.0013077940000000001
ppn tag 0.001302999
rich language 0.001293609
baseline tagging 0.0012930630000000001
statistical approach 0.001289059
indian language 0.001286426
national language 0.0012833039999999999
learning methods 0.001279143
disambiguation task 0.001276464
corpus overall 0.001262175
tic rules 0.001259614
lexical categories 0.001244257
language phenomena 0.0012428349999999999
japanese language 0.00123747
powerful data 0.001234183
baseline accuracy 0.001229491
language spe 0.001226287
training instances 0.001225411
analysis table 0.001220981
markov model 0.001220961
the rules 0.0012172250000000002
stochastic tagger 0.0012156480000000002
tion rules 0.001215281
linguistic analysis 0.0012127140000000002
replacement rules 0.001210868
driven tagger 0.0012084630000000001
context information 0.001207468
ment rules 0.001206114
training instance 0.001198277
learning curve 0.001192761
machine learning 0.001174509
semantic information 0.0011734900000000001
learning techniques 0.001157349
