Word Representation refers to the method of converting words into numerical formats that can be processed by computers. This is essential in natural language processing (NLP) tasks, as it allows algorithms to understand and manipulate text data. Common techniques include one-hot encoding, word embeddings, and bag-of-words models. Word embeddings, such as Word2Vec and GloVe, capture semantic relationships between words by placing them in a continuous vector space. This means that words with similar meanings are located closer together in this space, enabling more effective analysis and understanding of language by machines.