Word Embeddings
Word embeddings are a type of representation of words in a vector space that captures the semantic and syntactic meaning of the words. It is a language modeling technique used in natural language processing and machine learning. Word embeddings are generated by training a neural network on a large corpus of text data, where each word is represented as a high-dimensional vector. The vectors are learned in such a way that words with similar meanings are closer to each other in the vector space. Word embeddings have been shown to be effective in various natural language processing tasks such as language translation, sentiment analysis, and named entity recognition.
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