In NLP, what is embedding used for?

Embedding is utilized in Natural Language Processing (NLP) primarily for the conversion of words into numerical vectors, which is crucial for any computational analysis of text. This process allows words to be represented in a numerical format that captures their meanings and relationships within a specified context.

In NLP, words are often contextually similar or behave similarly in various linguistic scenarios. Embedding techniques, like Word2Vec or GloVe, facilitate the mapping of words into a continuous vector space where semantically similar words are positioned close to each other. This representation is integral to numerous NLP tasks, such as sentiment analysis, language translation, and text classification, enabling models to process and learn from textual data effectively.

Other options, while they may relate to various aspects of data handling and machine learning, do not specifically pertain to the concept of embedding as it is defined in the context of NLP. Storing large datasets and visualizing data are separate processes that do not involve the semantic representation of text, and preparing text for machine learning models could encompass various methodologies and transformations that go beyond just embedding. Therefore, converting words into numerical vectors is distinctly the function of embeddings in NLP.