DataTerminology.comNEW
A training technique that samples synthetic non-observed negatives to approximate costly normalization or balance classes.
DESCRIPTION
Negative sampling replaces full normalization with a small set of sampled negative examples, reducing computation for large vocabularies or catalogs. Used in word2vec, recommenders, and contrastive tasks, it trains models via binary classification of positive versus sampled negative pairs. Choosing the noise distribution and sample count affects quality and speed. It is closely related to sampled softmax and noise-contrastive estimation.
ALTERNATIVE DEFINITIONS
arXiv (Mikolov et al.)
We propose a simple and efficient method for learning word representations, called negative sampling, where only a few output words are sampled for each training example.
Introduces negative sampling for word embedding training efficiency.
TensorFlow
Negative sampling trains a binary classifier to distinguish target words from noise samples, avoiding the full softmax over the vocabulary.
Explains practical training mechanics in word2vec using sampled negatives.
Gensim
Gensim’s word2vec uses negative sampling or hierarchical softmax to train embeddings efficiently on large corpora.
Highlights implementation choices and scalability in a popular library.
RELATED TERMS
Word2Vec Skip-Gram Contrastive Learning Sampled Softmax Embeddings Implicit Feedback Recommendation System Log Loss
COMPETING TERMS
Noise Contrastive Estimation Hierarchical Softmax Full Softmax
RELATIONS
- Broader: Sampling
KNOWLEDGE AREAS
TARGET AUDIENCE
Data Scientist AI Engineer MLOps Engineer
EXTERNAL SOURCES
- Wikipedia - Word2vec
- arXiv - Original word2vec method
- TensorFlow - Word2vec tutorial with negatives
- Google Developers - Glossary definition
TOP QUESTIONS
Why use Negative Sampling over full softmax?
It drastically reduces computation in large output spaces, enabling fast training with comparable performance when the noise distribution is well-chosen.
Category: Relevancy
How many negatives should be sampled?
Common choices range from 5 to 20 per positive, but optimal values depend on vocabulary size, task, and desired training speed.
Category: Requirements
What is a common mistake with Negative Sampling?
Using a uniform noise distribution often underperforms; frequency-aware or temperature-adjusted distributions typically work better.
Category: Mistakes
Is Negative Sampling the same as NCE?
They are closely related, but NCE estimates the normalization constant explicitly, while negative sampling frames training as binary classification against sampled noise.
Category: Misunderstanding
How does Negative Sampling apply to recommendations?
It samples non-interacted items as negatives, enabling efficient training for implicit feedback models like matrix factorization or deep recommenders.
Category: Example
What are the risks of poor negative sampling?
Badly chosen negatives can yield misleading gradients, slow convergence, and biased embeddings. Monitor loss and evaluate downstream quality.
Category: Risk
What’s a competing technique to Negative Sampling?
Hierarchical softmax reduces complexity via tree-based factorization; sampled softmax approximates normalization differently.
Category: Alternatives
How to tune Negative Sampling in practice?
Experiment with k, noise distributions, and learning rates. Validate embedding quality on intrinsic tests and downstream tasks.
Category: HowTo
What’s trending around Negative Sampling?
Contrastive representation learning and hard negative mining strategies for better embedding separation.
Category: Topic