[tanananinena]

Classical word embeddings are static - their value doesn't change depending on the context they appear in.

You can think of the word embedding as a weighted average of embeddings of words which co-occur with the initial word.

So it's a bit of a blurry meaning.

Is "bark" related to a dog? Or to a tree?

Well, a bit of both, really. The embedding doesn't care about the context of the word - once it's been trained.

So if you search for related documents based on word embeddings of your query - it can happen that you miss the mark. The embeddings simply don't encode the semantics you need.

In fact, this can happen even with contextual embeddings, when you look for something specific or in a specialized domain. With word embeddings it's just much more apparent.