Intermediate · NLP
Word embedding
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. Vector representations of words where similar meanings correspond to nearby vectors.
Technical Definition
Vector representations of words where similar meanings correspond to nearby vectors.
How it works
A word embedding is a technique in natural language processing used to represent words as numerical vectors in a multi-dimensional space. The key idea is that words with similar meanings or that appear in similar contexts will have vectors that are close to each other in this space. This allows machine learning models to understand semantic relationships between words and process text more effectively. Popular examples include Word2Vec and GloVe.
Related Concepts
- Natural language processing (NLP) — A field of AI enabling computers to understand, interpret, and generate human language.
- Representation Learning — The process of automatically discovering meaningful representations of data from raw inputs.
- Vector Space Model — A mathematical model for representing text documents as vectors of identifiers, such as index terms.