What are embeddings in the context of NLP?

Embeddings in the context of Natural Language Processing (NLP) refer to representation techniques where words or phrases are transformed into vectors in a multidimensional space. This transformation allows for the capturing of semantic relationships between language tokens, meaning that words with similar meanings or uses will have similar vector representations. For instance, in a vector space, the distance between the vectors representing "king" and "queen" will be closer than that between "king" and "apple," reflecting the hierarchy of relationships and contextual meanings among words.

This method of representing language enables various NLP tasks, such as text classification, sentiment analysis, and machine translation, to leverage complex relationships embedded in the data. By mapping words into a shared numerical space, algorithms can perform mathematical operations that help identify similarities and patterns, facilitating better understanding and processing of human language.