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: 
> 
> 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)
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.