word features 0.00452104
character word 0.00392787
word feature 0.0038418899999999997
joint word 0.0038091799999999997
chinese word 0.003728257
word segmentation 0.003623897
first word 0.003450759
word sequence 0.0034128609999999997
previous word 0.0033262229999999997
word boundary 0.0032847429999999997
word candidate 0.003275457
standard word 0.00326373
second word 0.003238444
word seg 0.003236433
word length 0.0032344739999999998
last word 0.003196965
word level 0.003158522
corresponding word 0.003153834
next word 0.003147671
word scores 0.0031436019999999997
word boundaries 0.003143276
automatic word 0.003143113
possible word 0.003139052
word sequences 0.003122779
ith word 0.003116457
word segmen 0.003101027
word segmenta 0.003099301
vious word 0.0030984059999999997
word segmentor 0.003093777
word segmentors 0.003092857
marked word 0.003092857
tandard word 0.003092857
word candi 0.003092857
word uni 0.003092857
pos tag 0.0030596300000000002
joint pos 0.00293868
tag features 0.0027876100000000003
pos tagging 0.002752893
pos sequence 0.002542361
pos tags 0.002492058
tagging features 0.0024808729999999998
boundary pos 0.002414243
pos tagger 0.002357718
state features 0.002332218
pipeline pos 0.002317659
joint model 0.0022894400000000002
pos pair 0.002277353
automatic pos 0.002272613
pos pairs 0.00227174
optimal pos 0.00225651
ambiguous pos 0.002249659
labeled pos 0.0022474440000000004
independent pos 0.002246365
perfect pos 0.002236421
parsing model 0.0022180669999999998
unary features 0.002207972
transition features 0.002180115
tag feature 0.0021084600000000004
segmentation model 0.0021041569999999997
tagging model 0.002103653
rule features 0.0020411879999999998
tion features 0.002040976
level features 0.002016002
important features 0.001998488
chinese character 0.001992567
character words 0.001986366
binary features 0.001983925
gold features 0.001982044
bigram features 0.001963265
features ftop 0.001959646
features fbottom 0.001955212
sparse features 0.001955169
sition features 0.001950779
joint parsing 0.001883427
linear model 0.0018129589999999998
joint segmentation 0.001769517
joint tagging 0.001769013
tag set 0.0017533330000000001
model parameter 0.001701552
tag information 0.0016962280000000001
features 0.00168926
markov model 0.001680556
tag sequence 0.0016794310000000001
training corpus 0.0016753760000000001
training data 0.0016707150000000001
pipeline model 0.001668419
tagging error 0.001667553
joint decoding 0.001656809
system performance 0.0016498200000000002
joint models 0.001623113
unified model 0.001619599
joint method 0.001611514
ging model 0.001609866
generative model 0.001602332
model methods 0.0015888809999999999
model though 0.001587915
model complex 0.00158715
hybrid model 0.001579291
inative model 0.001573737
parsing results 0.0015646380000000001
