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word vectors 0.003733062
function word 0.003649935
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word class 0.003434482
content word 0.0034188969999999997
previous word 0.003411599
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word pairs 0.003332243
model vector 0.0033248799999999997
word sequences 0.00331773
context model 0.00322597
space model 0.002790652
model ing 0.0026011159999999997
simple model 0.002553186
trigram model 0.00253441
bigram model 0.0025275489999999996
trigraan model 0.002423392
lmlguage model 0.0024230009999999997
algebraic model 0.002419405
granl model 0.0024118639999999997
bigrana model 0.0024118639999999997
bigrmn model 0.0024118639999999997
context language 0.00220883
model 0.00220772
context vector 0.00213541
language models 0.002040985
function words 0.001890775
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training corpus 0.001712531
vector space 0.001700092
content words 0.0016597369999999999
vector size 0.0016401610000000002
certain words 0.001611394
text language 0.0016109789999999998
frequent words 0.001603394
related words 0.0015840079999999998
local language 0.0015561669999999998
tent words 0.0015534049999999999
unrelated words 0.001550007
tween words 0.0015462089999999998
vector representation 0.001544519
trigram language 0.0015172699999999998
bigram language 0.0015104089999999999
statistical language 0.001498815
language modeling 0.001492888
novel language 0.0014684199999999998
language nmdel 0.001428296
learning corpus 0.0014207019999999998
nonlocal language 0.001413365
cache language 0.0013982589999999998
tical language 0.001395075
language nmdels 0.0013945399999999999
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words 0.00134112
probability distribution 0.001253805
different probability 0.0012467350000000001
semantic representation 0.001238966
whole context 0.001228416
left context 0.001223307
vague context 0.001222252
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language 0.00119058
trigram models 0.001177095
sequence probability 0.001149883
training cor 0.001127843
vector 0.00111716
guage models 0.001111451
laalguage models 0.0010957270000000001
newspaper corpus 0.001087899
vocabulary standard 0.001069485
space approach 0.001065333
trigger models 0.001056008
cosine similarity 0.001044521
context 0.00101825
random vectors 0.00100559
learning method 0.001004804
conditional probability 9.93764E-4
matrix reduction 9.93759E-4
original matrix 9.78693E-4
important information 9.77545E-4
probability dis 9.76695E-4
occurrence probability 9.696770000000001E-4
standard trigram 9.554920000000001E-4
standard bigram 9.48631E-4
matrix elements 9.30693E-4
occurrence matrix 9.163940000000001E-4
training 8.82286E-4
vocabulary vocabulary 8.81366E-4
sense dismnbiguation 8.750920000000001E-4
first point 8.706289999999999E-4
different topics 8.61943E-4
occurrence vectors 8.598330000000001E-4
contextual information 8.55637E-4
standard tri 8.54131E-4
models 8.50405E-4
target vocabulary 8.35026E-4
tile learning 8.32503E-4
