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markov model 0.002380008
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network model 0.0023308649999999997
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compound words 0.002118977
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word problem 0.002044068
model 0.00203895
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words 0.00183629
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university corpus 0.001516522
feature set 0.0014820229999999998
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corpus length 0.00127206
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edr corpus 0.001217094
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tionary information 9.90821E-4
common noun 9.7643E-4
training sentences 9.76196E-4
corpus 9.64239E-4
combined method 9.63525E-4
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gram information 9.53678E-4
empirical probability 9.52322E-4
several inconsistencies 9.51983E-4
latter method 9.50974E-4
feature 9.3381E-4
sis method 9.30384E-4
possible histories 9.06808E-4
possible futures 9.04921E-4
optimal set 8.996379999999999E-4
precision table 8.92948E-4
such entries 8.874950000000001E-4
training cor 8.78654E-4
learning curve 8.78316E-4
large corpora 8.77884E-4
kyoto university 8.604159999999999E-4
conditioning data 8.58874E-4
same score 8.542809999999999E-4
little training 8.53211E-4
good use 8.48917E-4
same charac 8.473179999999999E-4
morpheme string 8.42838E-4
only morphemes 8.4145E-4
low probability 8.36571E-4
maximum length 8.33176E-4
large amount 8.29669E-4
current morpheme 8.27221E-4
sentence analysis 8.25877E-4
such incon 8.21132E-4
ture set 8.16184E-4
oto university 8.11805E-4
conditional probability 8.10955E-4
cal analysis 8.079700000000001E-4
