Question: I just met a terminology called "embedding" in a paper regarding deep learning. The context is "multi-modal embedding"

My guess: embedding of something is extract some feature of sth,to form a vector.

I couldn't get the explicit meaning for this terminology and that stops me from fully understanding the author's idea and model mechanism

I check the dictionary and search on line,but the explanation is based more on the real life meaning rather than meaning as a machine learning terminology.

And that raise a more generalized and frequently met question, when you find some machine learning terminology/word that you can't understand well, where can you get the solution, some specific way to google? join a machine learning group? raise a question in stack exchange?

Answer: 
According to all answers(Thank you) and my google search I got a better understanding, So my newly updated understanding is:

The embedding in machine learning or NLP is actually a technique mapping from words to vectors which you can do better analysis or relating, for example, "toyota" or "honda" can be hardly related in words, but in vector space it can be set to very close according to some measure, also you can strengthen the relation ship of word by setting: king-man+woman = Queen.

so we can set boy to (1,0) and then set girl to (-1,0) to show they are in the same dimension but the meaning is just opposite. And all nouns that just diff in gender can be parallel~

My initial guess that embedding is extracting features from something is close but not specific enough.

And for my last point when you met a jargon in some special area how to quickly get the essential meaning of it, I still didn't find a very good way, maybe a website that can explain the meaning of jargon in that area will save great time for us.
> 
> The LSA community seems to have first used the word “embedding” in Landauer
> et al. (1997), in a variant of its mathematical meaning as a mapping from one space or mathematical structure to another. In LSA, the word embedding seems to have described the mapping from the space of sparse count vectors to the latent space of SVD dense vectors. Although the word thus originally meant the mapping from one space to another, it has metonymically shifted to mean the resulting dense vector in the latent space. and it is in this sense that we currently use the word.
> 
> 
> 

For the more generalized and frequently met questions, I'd like to recommend you read textbooks, especially classic ones.

Refrence:  

[Speech and Language Processing: An introduction to natural language processing](https://web.stanford.edu/%7Ejurafsky/slp3/ed3book.pdf)