What is Word2Vec?
Word2Vec is a set of neural network models that learn word embeddings—continuous vector representations of words—based on their context within a corpus. The two main architectures of Word2Vec are:
- Continuous Bag of Words (CBOW): Predicts the target word from its context.
- Skip-Gram: Predicts the context words given a target word.
Both models aim to maximize the probability of word-context pairs observed in the training corpus.
Negaitve Sampling Using word2vec
Word2Vec, developed by Tomas Mikolov and colleagues at Google, has revolutionized natural language processing by transforming words into meaningful vector representations. Among the key innovations that made Word2Vec both efficient and effective is the technique of negative sampling. This article delves into what negative sampling is, why it’s crucial, and how it works within the Word2Vec framework.
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