CBOW and skip-gram are models of the Word2vec framework used in natural language processing. Word2Vec is a neural network model for word embeddings. Before diving into explaining what are embeddings, I have a question for you. How do we make machines understand text?
The core idea behind word embeddings is to convert text to numerical data (vector space) and capture the semantic as well as syntactic meaning of words and their relationships with other words in a corpus.
In neural network models like CBOW and skip-gram, the input layer is fed with the word embeddings. We generally use the one-hot encoding representation of the textual data to train the neural network models.
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18 апр 2024
