In NLP, embeddings are used to represent words, phrases, sentences, or entire documents as numerical vectors that capture semantic meaning. By embedding words or phrases in a continuous vector space, NLP models can understand and process relationships between words based on their proximity in the vector space. For instance, word embeddings like Word2Vec or GloVe map similar words, like "king" and "queen," to vectors that are close to each other, capturing their semantic similarity.
In more advanced NLP tasks, embeddings for longer sequences, such as sentences or paragraphs, are used. Models like BERT or GPT generate contextual embeddings, where the vector representation of a word depends on the surrounding context, enabling the model to understand ambiguous words based on their use in a sentence. These embeddings are used in applications such as text classification, named entity recognition, question answering, and machine translation.
The use of embeddings in NLP helps reduce the dimensionality of textual data while preserving important linguistic relationships. Embeddings make it easier to handle and process large amounts of unstructured text, enabling more efficient and accurate natural language understanding. They are essential for applications like search engines, chatbots, and automated content generation, where understanding the meaning of text is crucial.