Word Embedding
Definition
Word embeddings represent words as learned numeric vectors based on their usage.
A dense vector representation of words or word forms learned from their usage context.
Simply explained
Words receive numeric addresses. From their usage, the system learns which words appear in similar contexts.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A search system uses domain-specific word vectors to expand synonym candidates.
Limits and pitfalls
- Static embeddings have only one vector per word and inherit corpus biases; proximity is not synonymy.
Origin & history
Neural word embeddings were widely adopted through word2vec and GloVe starting in 2013/2014.
Use in mid-sized companies
- Possible example: A search system uses domain-specific word vectors to expand synonym candidates.
Frequently asked questions
What does word embedding mean?
A dense vector representation of words or word forms learned from their usage context. What should be considered? Static embeddings have only one vector per word and inherit corpus biases; proximity is not synonymy.
Sources
- Distributed Representations of Words and Phrases · Mikolov et al., Distributed Representations of Words and Phrases
Source verified on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
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