Word Embedding

Word embedding is a method of representing text in numerical form by mapping words to vectors of real numbers. This technique converts words into numerical vectors that can be used for various natural language processing tasks, such as text classification, clustering, and machine translation. By using word embedding, machines can gain an understanding of context and meaning in a document, which is essential for natural language processing (NLP). Word embedding is also useful for identifying synonyms and capturing the similarity between words in a corpus. This technique has applications that range from text analysis and sentiment analysis to knowledge representation and question-answering.