Text embeddings convert words or phrases into numerical vectors in a high-dimensional
space, where each dimension represents a semantic feature extracted by a model like
MiniLM-L6-v2. In this concept space, words with similar meanings have vectors that
are close together, allowing for quantitative comparisons of semantic similarity.
These vector representations enable powerful applications in natural language processing,
including semantic search, text classification, and clustering, by leveraging the
geometric properties of the embedding space to capture and analyze the relationships
between words and concepts.
Text Embeddings, Classification, and Semantic Search
(Youtube)
Text embeddings convert words or phrases into numerical vectors in a high-dimensional space, where each dimension represents a semantic feature extracted by a model like MiniLM-L6-v2. In this concept space, words with similar meanings have vectors that are close together, allowing for quantitative comparisons of semantic similarity. These vector representations enable powerful applications in natural language processing, including semantic search, text classification, and clustering, by leveraging the geometric properties of the embedding space to capture and analyze the relationships between words and concepts. Text Embeddings, Classification, and Semantic Search (Youtube)