Word2Vec is a method developed by researchers at Google that represents words as vectors in a high-dimensional space. This technique captures the semantic relationships between words, meaning that words with similar meanings will have similar vector representations. For example, the relationship between "king" and "queen" can be understood mathematically, as in the analogy "king - man + woman = queen".

Word2Vec uses a neural network to learn word representations from large amounts of text. It operates on the principle of the distributional hypothesis, which states that words that appear in similar contexts tend to have similar meanings. There are two main architectures used in Word2Vec:

1. Continuous Bag of Words (CBOW): This model predicts a target word based on its surrounding context words. For example, in the sentence "The cat sat on the mat," CBOW would use the words "The," "sat," "on," "the," and "mat" to predict the word "cat."
2. Skip-Gram: This model works in the opposite direction, predicting the surrounding context words given a target word. It is particularly effective for representing rare words and capturing more nuanced relationships between words.

Word2Vec has revolutionized natural language processing (NLP) by providing a way for machines to understand language in a more human-like manner. By converting words into vectors, it enables various applications such as text similarity, sentiment analysis, and machine translation. The ability to perform mathematical operations on word vectors allows for advanced language tasks, making Word2Vec a foundational tool in modern NLP.In summary, Word2Vec is a powerful technique that helps machines comprehend language by mapping words to numerical representations, facilitating a deeper understanding of word meanings and relationships.