What is Embedding in Natural Language Processing (NLP)?

Embedding refers to the process of representing data or information in a compact and efficient way. In natural language processing, embedding refers to the process of converting words or phrases into numerical vectors that can be easily processed by machine learning models. These numerical vectors capture the semantic meaning of words and their relationships with other words in a given text corpus. The most commonly used embedding techniques in NLP include Word2Vec, GloVe, and fastText. These techniques have been widely used in various NLP tasks such as sentiment analysis, text classification, and machine translation.