many features 0.00299568
different features 0.002971978
such features 0.00291295
basic features 0.002809352
following features 0.002804483
linguistic features 0.002766818
additional features 0.002754842
binary features 0.00270194
order features 0.002692641
type features 0.002679962
role features 0.0026628190000000003
relevant features 0.002651606
tic features 0.002644728
irrelevant features 0.00262325
hanced features 0.002607196
nonzero features 0.002607196
conjunction features 0.002607196
possible feature 0.002540036
feature vector 0.002489781
feature value 0.002466309
large feature 0.0023932429999999998
current feature 0.002382774
features 0.00237432
above feature 0.002372014
feature space 0.002334601
feature cents 0.002333881
order feature 0.002329361
dimensional feature 0.002311058
corresponding feature 0.00229448
incoming feature 0.002282123
feature vectors 0.002272115
feature spaces 0.002257025
feature list 0.002255837
feature lists 0.002252346
constant feature 0.002251806
feature vec 0.002248988
sociated feature 0.002244428
training data 0.0021559450000000003
feature 0.00201104
input data 0.0017688920000000002
training time 0.001639824
training set 0.001611441
data point 0.001586198
labeled data 0.001584459
first word 0.00148675
separable data 0.0014763040000000001
noisy data 0.001468893
input training 0.001461437
training vector 0.001402986
same time 0.0013327170000000002
winnow algorithm 0.001315768
standard training 0.0013008339999999998
winnow results 0.001292309
ing time 0.001244135
winnow method 0.001156624
weight vector 0.001135764
linear weight 0.001112536
different chunk 0.0011048870000000001
test set 0.0011037780000000001
learning methods 0.001084216
machine learning 0.001081642
loss function 0.00107873
many machine 0.001077864
dependency structure 0.0010761400000000002
classification problem 0.001055453
new method 0.001050458
possible pos 0.001049063
learning system 0.001042102
other parser 0.001027696
experimental results 0.0010187479999999999
different algorithms 0.001016273
input vector 0.0010159330000000001
criminant function 0.001015641
function misclassifies 0.001015641
pos tag 0.001011597
prediction results 0.001009307
class classification 0.0010083549999999998
esg dependency 0.001000856
winnow algorithms 9.99956E-4
chunk tag 9.98759E-4
time complexity 9.93985E-4
positive weight 9.93087E-4
algorithm updates 9.90085E-4
other approaches 9.871279999999999E-4
winnow approach 9.86762E-4
text sequence 9.84097E-4
initial weight 9.79104E-4
pos value 9.75336E-4
time complex 9.710680000000001E-4
previous chunk 9.62208E-4
same setup 9.59166E-4
clock time 9.50353E-4
learning techniques 9.50158E-4
classification accuracy 9.46011E-4
possible test 9.45578E-4
other researchers 9.45296E-4
other aspects 9.440389999999999E-4
winnow classifier 9.41591E-4
vector machine 9.352449999999999E-4
true label 9.3116E-4
