Advanced · NLP
Contextualized language embedding
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. Word representations that capture meaning based on surrounding text, going beyond static embeddings to understand nuance and context.
Technical Definition
Word representations that capture meaning based on surrounding text, going beyond static embeddings to understand nuance and context.
How it works
Contextualized language embeddings represent words and phrases in a way that captures their meaning within a specific sentence or context, much like human understanding. Unlike older methods (e.g., word2vec) that assign a single vector to each word, contextualized embeddings can produce different vectors for the same word depending on its usage. This allows them to grasp complex syntax, semantics, and the subtle nuances of language.
Related Concepts
- Word embedding — Vector representations of words where similar meanings correspond to nearby vectors.
- Semantics — The study of meaning in language, focusing on how words, phrases, and sentences convey sense.