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: 
In the context of machine learning, an embedding is a low-dimensional, learned continuous vector representation of discrete variables into which you can translate high-dimensional vectors. Generally, embeddings make ML models more efficient and easier to work with, and can be used with other models as well.

Typically, when I stumble upon jargon I'm not familiar with I first turn to Google, and if it can't be found I ping my colleagues and data science forums.
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.