A One Hot Vector

Type:	Algoliterary exploration
Technique:	word-embeddings
Developed by:	Algolit

Making a one hot vector

If this is our example sentence ...

"The algoliterary explorers discovered a multidimensional landscape made of words disguised as numbers."

... these are the 14 words we work with ...

a
algoliterary
as
discovered
disguised
explores
landscape
made
multidimensional
numbers
of
the
words
.

... a single vector in a one-hot-vector looks like this ...

[0 0 0 0 0 0 0 0 0 0 0 0 0 0] 

... and a full fourteen-dimensional matrix like this ...

[[0 0 0 0 0 0 0 0 0 0 0 0 0 0]  a
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0]  algoliterary
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0]  as
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0]  discovered
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0]  disguised
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0]  explores
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0]  landscape
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0]  made
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0]  multidimensional
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0]  numbers
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0]  of
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0]  the
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0]  words
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0]] .

... with one 0 for each unique word in a vocabulary, and a row for each unique word.

The following step is to count how often a word appears next to another ...

"The algoliterary explorers discovered a multidimensional landscape made of words disguised as numbers."

[[0 0 0 1 0 0 0 0 1 0 0 0 0 0]  a
 [0 0 0 0 0 1 0 0 0 0 0 1 0 0]  algoliterary
 [0 0 0 0 1 0 0 0 0 1 0 0 0 0]  as
 [1 0 0 0 0 1 0 0 0 0 0 0 0 0]  discovered
 [0 0 1 0 0 0 0 0 0 0 0 0 1 0]  disguised
 [0 1 0 1 0 0 0 0 0 0 0 0 0 0]  explores
 [0 0 0 0 0 0 0 1 1 0 0 0 0 0]  landscape
 [0 0 0 0 0 0 1 0 0 0 1 0 0 0]  made
 [1 0 0 0 0 0 1 0 0 0 0 0 0 0]  multidimensional
 [0 0 1 0 0 0 0 0 0 0 0 0 0 1]  numbers
 [0 0 0 0 0 0 0 1 0 0 0 0 1 0]  of
 [0 1 0 0 0 0 0 0 0 0 0 0 0 0]  the
 [0 0 0 0 1 0 0 0 0 0 1 0 0 0]  words
 [0 0 0 0 0 0 0 0 0 1 0 0 0 0]] .

Algolit one-hot-vector scripts

Two one-hot-vector scripts were created during one of the Algolit sessions, both creating the same matrix but in a different way. To download and run them, use the following links: one-hot-vector_gijs.py & one-hot-vector_hans.py

Note that

"Words are represented once in a vector. So words with multiple meanings, like "bank", are more difficult to represent. There is research to multivectors for one word, so that it does not end up in the middle." (Richard Socher, idem.)]

For more notes on this lecture visit http://pad.constantvzw.org/public_pad/neural_networks_3