The Role of Negative Sampling

Training Word2Vec models, especially the Skip-Gram model, involves handling vast amounts of data. This poses a computational challenge, particularly when calculating the softmax function over a large vocabulary, which is computationally expensive. Negative sampling addresses this by simplifying the problem.

What is Negative Sampling?

Negative sampling is a technique that modifies the training objective from predicting the entire probability distribution of the vocabulary (as in softmax) to focusing on distinguishing the target word from a few noise (negative) words. Instead of updating the weights for all words in the vocabulary, negative sampling updates the weights for only a small number of words, significantly reducing computation.

How Negative Sampling Works?

In negative sampling, for each word-context pair, the model not only processes the actual context words (positive samples) but also a few randomly chosen words from the vocabulary that do not appear in the context (negative samples). The modified objective function aims to:

• Maximize the probability that a word-context pair (target word and its context word) is observed in the corpus.
• Minimize the probability that randomly sampled word-context pairs are observed.

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.