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eduroam-prg-gm-1-3-245.net.univ-paris-diderot.fr 2025-9-19:10:2:53

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M1 LOGOS .machine learning for NLP.md
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@@ -15,6 +15,10 @@ $x_{i} \in \mathbb{R}$ is a scalar
one-hot : boolean vector with all zeroes but one value. Usefull if each dimension represents a word of the vocabulary
BOW : Bag Of Words
You could represent sentences like that :
Let our vocabulary be : `V = 'le' 'un' 'garcon' 'lit' 'livre' 'regarde'`
Then "le garcon lit le livre" would be written by counting the number of occurences of each word of the sentence in a vector, so `2 0 1 1 1 0` (the formula is )
Then "le garcon lit le livre" would be written by counting the number of occurences of each word of the sentence in a vector, so `2 0 1 1 1 0` (the formula is `sentence +⌿⍤(∘.≡) vocabulary`)
$\cos(u, v) = \frac{u\cdot v}{\|u\| \| v\|}$