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.
Embeddings are vector representations of a particular word.

In Machine learning, textual content has to be converted to numerical data to feed it into Algorithm. 

One method is one hot encoding but it breaks down when we have large no of vocabulary. The size of word representation grows as the vocabulary grows. Also, it is sparse.

With embedding (fixed size vectors with lower dimension), the size of word representation can be controlled. Also, the vector representation stores the semantic relationship b/w words. There are pretrained embeddings Word2Vec, Glove etc available which can be used just as a lookup. Embeddings improve the performance of ML model significantly.