What is an Embedding?
Embedding (also called Vector Embeddings) is a series of vectors providing a mathematical representation of words or sentences. The vectors capture the semantic meaning and context of the words or phrases.
The benefit of Vector Embeddings is that they allow us to compare and analyse words and phrases mathematically, enabling us to perform tasks such as natural language processing, text classification, and information retrieval. They also allow us to identify similarities and relationships between words, even if they are quite different.
For example, the Vector Embeddings for “dog” and “cat” would be much closer to each other than that of “dog” and “banana”. Even though dog and cat do not share the exact spelling, meaning, and space in the dictionary, they are not synonyms. However, they share characteristics like they’re both four-legged, can both be kept as pets, can be trained etc.
Now that we understand the basic concept of Embeddings let’s look at how we can use this to simplify painful Data Management activities.