Learning Word Embeddings Doubt

Hi Mentor,

In the lecture video, Learning Word Embeddings, at video minute 5:29, below notes we didnt get. can you please what does it meant ?

And it turns out that this algorithm we’ll learn pretty decent word embeddings.
And the reason is, if you remember our orange juice, apple juice example, is in the algorithm’s incentive to learn pretty similar word embeddings for orange and apple because doing so allows it to fit the training set better because it’s going to see orange juice sometimes, or see apple juice sometimes, and so, if you have only a 300 dimensional feature vector to represent all of these words, the algorithm will find that it fits the training set best. If apples, oranges, and grapes, and pears, and so on and maybe also durians which is a very rare fruit and that with similar feature vectors

I think he’s trying to describe that different types of fruit are going to have similar embeddings.

For example, you might see “I like apple pie” or “I like cherry pie” in the training set, but you are not likely to find something like “I like paper pie”.