Semantic Similarity
Semantic similarity is a measure of how close two pieces of text are in meaning, regardless of whether they share the same words. AI systems calculate semantic similarity by converting text into numerical vectors (embeddings) and measuring the distance between them — texts with similar meanings produce vectors that are close together in a high-dimensional space. This capability powers search engines, recommendation systems, duplicate detection, and retrieval-augmented generation pipelines.
Example
The phrases "How do I cancel my subscription?" and "I want to stop my monthly plan" use completely different words but mean nearly the same thing. A semantic similarity model scores them at 0.94 out of 1.0 because their embedding vectors are close together, while "What's the weather today?" scores only 0.12 against either — correctly identifying it as unrelated.
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