Reproduce “word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method” on OpenResearch
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About this paper
Yoav Goldberg, Omer Levy
This paper clarifies the mathematical foundations of the negative sampling technique used in the Skip-gram variant of word2vec, providing a detailed derivation that was previously unarticulated in the original publications. The work explains how this approach reduces computational complexity by converting the task into a binary classification problem, allowing for more efficient word embedding generation.
Key insights
- Negative sampling transforms the computationally expensive Skip-gram objective into a binary classification task, distinguishing true word-context pairs from randomly sampled 'negative' pairs.
- The paper provides a detailed mathematical derivation showing how the negative sampling objective is formulated to maximize the probability of real pairs and minimize that of negative samples.
- Beyond the core derivation, the work clarifies subtle but impactful implementation details of word2vec, such as dynamic window size, subsampling of frequent words, and pruning of rare words.
Summary by alphaXiv.
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