Yesterday I wrote about the canonical example of how vector embeddings of words add:
“king” − “man” + “woman” ≈ “queen”
This should be interpreted as saying that the word vector for king, minus the word vector for man, plus the word vector for woman, is in some sense close to the word vector for queen.
This post will look at another example. Is the expression
“coffee” + “milk” ≈ “latte”
true in some sense?
Notation
In this post I will use “foo” to mean the vector embedding of the word foo.
Coffee + milk
The cosine similarity between “coffee” + “milk” and “latte” is about 0.63. And for reasons given in the previous post, this is a large value of cosine similarity. But there are 11 words that are more similar to “milk” + “coffee” than “latte”. Here are the top 12 matches in order.
- coffee
- milk
- tea
- drink
- chocolate
- cream
- breakfast
- ice
- beer
- vanilla
- starbucks
- latte
There are two questions to resolve. First, why isn’t latte one of the closest words? Second, why is the cosine similarity large even though latte is not one of the best matches?
Concept arithmetic
When word vector arithmetic works, as in the king and queen example, the vectors combine concepts. If you replace the male gender component of king with a female component, you get a vector close to the vector for queen.
But when you add the vectors for milk and coffee, you’re not adding concepts, you’re adding ingredients.
The concepts of milk and coffee are similar in that they’re both common beverages, as are tea and even beer. A latte is a beverage, but it’s not as common as milk, coffee, tea, or beer.
Extremely uneven distribution
If you divide word vectors by their norm, you get a point on a high-dimensional sphere. In the case of the glove-twitter-200 vector embedding, you get a point on a sphere in 200 dimensions. As explained in the earlier post, a fairly large cosine similarity corresponds to a tiny portion of the sphere’s surface area.
In the example of “king” − “man” + “woman”, the vector “queen” is the closest match (except for “king” itself).
But there are a lot of words whose vectors are within a tiny region around “coffee” + “milk”. And by tiny, I mean a region that accounts for a proportion of the sphere on the order of 10−23.
The glove-twitter-200 vector list contains vectors for 1.2 million words. If these vectors were roughly evenly distributed on the sphere when normalized, you’d expect each patch representing 10−6 of the sphere to contain about a word or two. You wouldn’t expect a patch taking up 10−12 of the sphere to contain more than one word, and you certainly wouldn’t expect a patch taking up 10−23 of the sphere to contain 12 words.
Rank order
Rank order based on cosine similarity is more robust than cosine similarity itself. This is an example of a phenomenon that occurs regularly: a metric whose values are dubious might still rank things well. Naive Bayes is another example. It naively computes probabilities in a way that is blatantly wrong, and yet ranking things by these spurious probabilities works well in some cases.
The cosine similarity between “king” − “man” + “woman” and “queen” is roughly the same as the cosine similarity between “coffee” + “milk” and “latte.” But in the former example, rank order picks out queen as the best match; rank order works like you’d expect, because you’re working with attributes that can be decomposed.
Dog + infant = puppy?
I wouldn’t be surprised if the Anglo-Saxon word for puppy was something like dogchild. The language was full of colorful compound words, such as hronrad (“whale-road”) for the sea and nosethyrl (“nose-hole”) for nostril.
Here are the top ten matches for “dog” + “infant” along with their cosine similarities.
- dog, 0.819
- infant, 0.809
- toddler, 0.734
- dogs, 0.697
- puppy, 0.688
- cat, 0.682
- pet, 0.676
- child, 0.671
- newborn, 0.670
- baby, 0.650
This shows that “puppy” is close to “dog” + “infant”, both in terms of cosine similarity and rank order, though it’s not the closet.
This also shows that you have to take the addition of word vectors with a grain of salt. It’s no surprise that puppy was a good match, but it’s surprising that cat is nearly as good.