Embedding
Embeddings, in the context of artificial intelligence and natural language processing ( NLP ), are numerical representations of objects , such as words, phrases, entire documents, images, or other data . These representations are vectors of real numbers in a lower-dimensional space, where the proximity between vectors reflects the semantic or contextual similarity between the objects they represent. In other words, objects with similar meanings or characteristics are mapped to nearby points in this vector space.
machine learning applications information , such as text. For words, for example, an embedding captures the contextual and semantic relationships of the word with other words, so that "king" and "queen" can have close vectors, as can "apple" and "orange". This is achieved through unsupervised or supervised learning techniques, where the model learns to map objects to these vectors in a way that preserves their properties and relationships.
The usefulness of embeddings is vast, being employed in tasks such as product recommendation (where similar items have close embeddings), information retrieval the query ), machine translation, sentiment analysis, and classification AI models to understand the meaning and context of data more effectively than traditional representations, such as word counting , which do not capture semantic relationships.
Sources:
- Google Developers. “Embeddings”. Available at: https://developers.google.com/machine-learning/glossary/embeddings . Accessed on: July 7, 2025.
- IBM Cloud Learn Hub. “What are embeddings in AI?”. Available at: https://www.ibm.com/cloud/learn/embeddings . Accessed on: July 7, 2025.
Post comment