This question is restricted to the text domain only.
The meaning of the word "encode" is Convert (information or instruction) into a particular form. One which performs encoding is called an encoder.
In deep learning, an encoder can also be the first part of a neural network (autoencoder) that simulates identity function, which governs the English meaning of encoder since it encodes the input.
Embeddings are encodings where the intention is to preserve semantics. You can observe the following excerpt from the chapter Vector Semantics and Embeddings
In this chapter we introduce vector semantics, which instantiates this linguistic hypothesis by learning representations of the meaning of words, called embeddings, directly from their distributions in texts.
But all encodings may not be the embeddings since encodings might not always preserve semantics (?). I have doubt in this statement which I inferred based on my current knowledge.
Many times, I came across the terms text encoding and text embedding interchangeably. But failing to catch whether they are the same or we need to be choosy while using them.
Consider the following usages of encoding and embedding in the paper titled Generative Adversarial Text to Image Synthesis by Scott Reed et al.
#1: The intuition here is that a text encoding should have a higher compatibility score with images of the correspondong class compared to any other class and vice-versa.
#2: Text encoding $\phi(t)$ is used by both generator and discriminator.
#3: ...where $T$ is the dimension of the text description embedding.
#4: ... we encode the text query $t$ using text encoder $\phi$. The description embedding $\phi(t)$ is first compressed ...
I think they are used interchangeably. Is it true? Can I use any word if I am confident enough that my encoding is semantic preserving? Or is there any strong reason for choosing the words?
If you observe the last point, the word "encoder" is used. Can I use embedder instead of it?