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Word Embeddings Under the Hood
How Neural Networks Learn from Language
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word2vec
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“Wait, what?”
Source: https://tinyurl.com/y9lb6e7j
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Good news! Text data is everywhere.
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Bad news… there is way too much.
We need computers to help!
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We started with the scallop dish as an appetizer, followed by
the spaghetti with tomato sauce and duck and foie gras ravioli.
How do we represent data like this?
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1 2 3 … V
we 1 0 0 … 0
started 0 1 0 … 0
with 0 0 1 … 0
… … … … … …
ravioli 0 0 0 … 1
One-Hot Encoding
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…but one-hot encoding
leaves a lot to be desired.
Are better word representations possible?
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y
-2
-1
0
1
2
x
-2 -1 0 1 2
beer
wine
cocktail
spoon
fork
knife
spaghetti
pasta
lasagna
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y
-2
-1
0
1
2
x
-2 -1 0 1 2
beer
wine
cocktail
spoon
fork
knife
spaghetti
pasta
lasagna
x y
spaghetti 1.0 1.5
pasta 1.2 1.3
… … …
fork 0.0 -0.7
spoon -0.5 -1.5
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“You shall know a word by the company it keeps.”
— J.R. Firth, 1957
Postulate #1
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“Neural networks learn useful, new data representations.”
— Rumelhart, Hinton & Williams, 1986
(paraphrased)
Postulate #2
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context clues
neural networks
?
=
+
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Context clues as training data?
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spaghetti followed 1
spaghetti by 1
… … …
spaghetti sauce 1
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spaghetti followed 1
spaghetti by 1
… … …
spaghetti sauce 1
spaghetti we 0
spaghetti parking 0
… … …
spaghetti sushi 0
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with by 1
with the 1
… … …
with and 1
with appetizer 0
with loud 0
… … …
with up 0
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Minimum Viable Introduction to
Neural Networks
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“Sigmoid” Activation Function
Weighted Input
Activation Value
(z) =
1
1 + e z
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“Sigmoid” Activation Function
Weighted Input
Activation Value
0.88
(z) =
1
1 + e z
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A neural network for
learning context clues?
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(spaghetti, tomato, 1)
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(weight matrix for the hidden layer)
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(weight matrix for the hidden layer)
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(weight matrix for the output layer)
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(weight matrix for the output layer)
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Training our network
on the first context clue
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Training our network
on the first context clue
(there will be lots of these)
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1. Make a prediction
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1. Make a prediction
2. Measure how wrong we are
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1. Make a prediction
2. Measure how wrong we are
3. Tune the model to become
slightly less wrong
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1. Make a prediction
2. Measure how wrong we are
3. Tune the model to become
slightly less wrong
4. Repeat with the next context clue
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1. Make a prediction
2. Measure how wrong we are
3. Tune the model to become
slightly less wrong
4. Repeat with the next context clue
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Weighted Input
Activation Value
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Weighted Input
Activation Value
0.51
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“forward pass”
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1. Make a prediction
2. Measure how wrong we are
3. Tune the model to become
slightly less wrong
4. Repeat with the next context clue
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“Loss” Function
Model Prediction
Penalty
L(ˆ
y) = ln(ˆ
y)
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right answer: 1
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“Loss” Function
Model Prediction
Penalty
right answer: 0
L(ˆ
y) = ln (1 ˆ
y)
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“Loss” Function
Model Prediction
Penalty
L(ˆ
y) = ln(ˆ
y)
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right answer: 1
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“Loss” Function
Model Prediction
Penalty
0.51
0.67
L(ˆ
y) = ln(ˆ
y)
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AAACAHicdVDLSgMxFM3UV62vUTeCm2AR6sIyI6J2IRTcuHBRwbFCO5RMmmlDM5khuSOUoW78FTcuVNz6Ge78G9OH4vPAhZNz7iX3niARXIPjvFm5qemZ2bn8fGFhcWl5xV5du9RxqijzaCxidRUQzQSXzAMOgl0lipEoEKwe9E6Gfv2aKc1jeQH9hPkR6UgeckrASC1746zU7BLI+oOd492mkJ+vll10yhXHrRy4+Ddxy84IRTRBrWW/NtsxTSMmgQqidcN1EvAzooBTwQaFZqpZQmiPdFjDUEkipv1sdMEAbxuljcNYmZKAR+rXiYxEWvejwHRGBLr6pzcU//IaKYRHfsZlkgKTdPxRmAoMMR7GgdtcMQqibwihiptdMe0SRSiY0AomhI9L8f/E2ytXys75frFam6SRR5toC5WQiw5RFZ2iGvIQRTfoDj2gR+vWureerOdxa86azKyjb7Be3gFNVZZs
right answer: 1
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1. Make a prediction
2. Measure how wrong we are
3. Tune the model to become
slightly less wrong
4. Repeat with the next context clue
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“Loss” Function
Model Prediction
Penalty
0.51
0.67
L(ˆ
y) = ln(ˆ
y)
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AAACAHicdVDLSgMxFM3UV62vUTeCm2AR6sIyI6J2IRTcuHBRwbFCO5RMmmlDM5khuSOUoW78FTcuVNz6Ge78G9OH4vPAhZNz7iX3niARXIPjvFm5qemZ2bn8fGFhcWl5xV5du9RxqijzaCxidRUQzQSXzAMOgl0lipEoEKwe9E6Gfv2aKc1jeQH9hPkR6UgeckrASC1746zU7BLI+oOd492mkJ+vll10yhXHrRy4+Ddxy84IRTRBrWW/NtsxTSMmgQqidcN1EvAzooBTwQaFZqpZQmiPdFjDUEkipv1sdMEAbxuljcNYmZKAR+rXiYxEWvejwHRGBLr6pzcU//IaKYRHfsZlkgKTdPxRmAoMMR7GgdtcMQqibwihiptdMe0SRSiY0AomhI9L8f/E2ytXys75frFam6SRR5toC5WQiw5RFZ2iGvIQRTfoDj2gR+vWureerOdxa86azKyjb7Be3gFNVZZs
right answer: 1
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“Loss” Function
Model Prediction
Penalty
0.51
0.67
L(ˆ
y)
ˆ
y
=
1
ˆ
y
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L(ˆ
y) = ln(ˆ
y)
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right answer: 1
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Move in the opposite direction
from the gradient
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It works! Now…
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It works! Now…
1. How do we know which direction
to nudge a weight?
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It works! Now…
1. How do we know which direction
to nudge a weight?
2. How can we calculate this
automatically for all the weights?
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“back propagation”
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Did it work?
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“stochastic gradient descent”
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Context clues trained: 1
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1. Make a prediction
2. Measure how wrong we are
3. Tune the model to become
slightly less wrong
4. Repeat with the next context clue
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Learning to predict Learning to represent
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we can measure distances!
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Context clues trained: 1
topics
discerning
masked
sweets
carmelized
shelly
cue
prepare
“amazing” as focus word: 0
cheerful
succulent
adjusting
pop
antenna
suggesting
vinegary
brothers
“server” as focus word: 0
ignorant
sop
refrigerators
bags
recliner
introduce
covered
petco
“spaghetti” as focus word: 1
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Fast forward…
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Context clues trained: 2,000,000
awesome
delicious
super
here
)
$
customer
excellent
“amazing” as focus word: 1,854
thru
along
crab
tacos
/
windows
chef
1
“server” as focus word: 780
dollar
rings
loves
=
opened
wrapped
form
provided
“spaghetti” as focus word: 84
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Fast forward…
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Context clues trained: 100,000,000
incredible
awesome
outstanding
excellent
phenomenal
fabulous
superb
fantastic
“amazing” as focus word: 87,864
waiter
waitress
bartender
hostess
guide
technician
cashier
barista
“server” as focus word: 48,492
risotto
veal
katsu
goat
turkey
enchilada
raspberry
meatloaf
“spaghetti” as focus word: 3,600
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bun american + mexican ⇡ tortilla
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“word2vec skip-gram negative sampling”
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No magical black box AI…
Just context clues and some arithmetic!
Bonus: now you know the fundamentals
of all neural network learning
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