semantic relations 0.0018252720000000002
other features 0.001792222
semantic information 0.0017821080000000001
interpretation model 0.001691856
noun pairs 0.001686721
unsupervised model 0.001684659
supervised model 0.001673452
learning model 0.001666869
unseen noun 0.0016668479999999999
english noun 0.001656448
same noun 0.001648453
semantic classes 0.001630753
first noun 0.001624672
semantic relation 0.001619785
head noun 0.00161686
svm model 0.001613924
semantic class 0.0016131680000000001
semantic categories 0.001597509
probabilistic model 0.001597256
linguistic corpus 0.001593378
noun phrase 0.00159239
semantic distribution 0.001584764
training corpus 0.001583905
noun pair 0.001577791
trigram model 0.001566334
pendent model 0.001566334
linguistic features 0.001564879
second noun 0.001545185
semantic interpretation 0.001533676
semantic classification 0.001531019
noun phrases 0.001528832
noun instances 0.001508024
modifier noun 0.001493644
noun compound 0.001489801
other relations 0.0014886909999999999
noun compounds 0.0014852490000000001
ambiguous noun 0.001483621
wordnet noun 0.001482911
noun senses 0.001482901
semantic knowledge 0.0014725740000000001
noun constituents 0.0014618819999999999
semantic lists 0.0014608680000000001
semantic category 0.0014555030000000001
noun con 0.001447265
noun com 0.001447027
test data 0.001433831
constituent noun 0.001430909
marked noun 0.001424645
semantic cat 0.0014246
semantic content 0.001424549
consecutive noun 0.001418625
noun hier 0.00141755
nominalized noun 0.001416923
symbolic noun 0.001416923
noun inflection 0.001416923
semantic scattering 0.001411981
semantic ambiguities 0.0014096430000000001
semantic taxonomies 0.0014081010000000001
semantic scatter 0.0014081010000000001
english corpus 0.001397
english features 0.001368501
model 0.00136202
training data 0.0013588229999999999
other set 0.001356821
test nouns 0.001343906
corpus distribution 0.001334386
class features 0.001334291
other english 0.001310797
other examples 0.0012692369999999999
such nouns 0.001265347
corpus analysis 0.001262108
corresponding features 0.001260294
standard corpus 0.001252088
corpus instances 0.001248576
models results 0.001244914
corpus statistics 0.001239497
linguistic information 0.001218184
corpus annotation 0.001215228
semantic 0.00120384
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test set 0.001195013
context features 0.0011927700000000001
europarl corpus 0.001186058
initial corpus 0.0011811459999999999
cluvi corpus 0.001176599
nominalization features 0.001145636
other fea 0.001145324
language pairs 0.001136003
tence features 0.001133248
pendency features 0.001131979
different corpora 0.001118432
different size 0.0011164880000000001
word target 0.001115018
corresponding word 0.001100294
other hand 0.001096113
learning models 0.001085885
english nouns 0.001081993
other sen 0.001081121
other researchers 0.001072271
other natu 0.001072271
