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word embeddings 0.004405984
word embedding 0.00408195
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word representations 0.004042694
continuous word 0.0040422290000000005
unsupervised word 0.00401114
discrete word 0.003983022
basic word 0.003972239
word clusters 0.003971189
word vectors 0.0039677210000000004
unknown word 0.0039135770000000005
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dimensional word 0.0039025920000000003
word identity 0.003889132
word emission 0.003887358
word hey 0.0038857460000000003
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training corpus 0.001828878
generative model 0.001810002
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training set 0.0017919049999999999
oov model 0.001780328
related words 0.0017614999999999998
emission model 0.001750128
robust model 0.00174886
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oov words 0.001689978
unknown words 0.0016859969999999998
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indicator features 0.001675627
group words 0.0016671589999999999
template features 0.0016242070000000001
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feature set 0.0015570150000000001
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full training 0.0015477289999999999
little training 0.001536705
labeled training 0.001526494
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training corpora 0.001498588
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training procedures 0.001495412
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words 0.00141955
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feature corre 0.001299718
training 0.00125636
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parser performance 0.00120426
syntactic information 0.0011956739999999999
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baseline parser 0.00110464
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test set 0.001071405
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data sets 0.001038022
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dependency parsing 0.001029217
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feature 0.00102147
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context window 0.00100766
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much data 9.99446E-4
original parser 9.97856E-4
neural network 9.85491E-4
brown corpus 9.82892E-4
natural language 9.80673E-4
berkeley parser 9.79558E-4
syntactic structure 9.78968E-4
small gains 9.771250000000001E-4
ing set 9.68739E-4
featured parser 9.67313E-4
corpus size 9.655940000000001E-4
