Embeddings have emerged as a powerful technique for representing linguistic units, such as words, sentences, or entire documents, as dense numerical vectors in a high-dimensional vector space. These embeddings capture the semantic meaning and relationships between different linguistic entities, allowing them to be processed and analyzed using machine learning techniques.

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Embeddings in NLP are powerful techniques for representing linguistic units as dense numerical vectors. They capture semantic meaning and relationships between entities. There are four main types of embeddings: token embeddings, word embeddings, sentence embeddings, and document embeddings. Embeddings can be learned through unsupervised or self-supervised learning methods. They offer advantages such as capturing semantic and contextual information, improving NLP model performance, and simplifying feature engineering. Embeddings are used in large language models, retrieval from vector databases, and various NLP applications.

Issue #58 - Embeddings in NLP