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Also known as: Word2Vec Skip-gram
A Word2Vec model that predicts surrounding context words from a center word to learn dense embeddings.
DESCRIPTION
Skip-gram trains embeddings by predicting context words given a target word, capturing semantic and syntactic relationships. It often uses negative sampling or hierarchical softmax for efficiency. The learned vectors enable similarity, analogies, and downstream tasks like classification. Compared to CBOW, skip-gram can represent rare words better at the cost of speed. It remains foundational in NLP embedding learning.
ALTERNATIVE DEFINITIONS
Wikipedia
In the skip-gram model, the model predicts surrounding words given the current word.
Concise explanation of skip-gram’s predictive objective.
Mikolov et al.
We use a continuous Skip-gram model, where we try to predict the surrounding words within a certain range from the current word.
Original formulation of the skip-gram training objective.
Skip-gram predicts context words, training word vectors efficiently with techniques like negative sampling.
Developer-centric summary highlighting efficient training.
RELATED TERMS
Word2Vec Negative Sampling Embeddings FastText Context Window Distributional Semantics Mikolov Natural Language Processing
COMPETING TERMS
KNOWLEDGE AREAS
TARGET AUDIENCE
Data Scientist AI Engineer MLOps Engineer
EXTERNAL SOURCES
- Wikipedia - Word2vec
- arXiv - Original Word2Vec paper
- Google - Word2Vec code and docs archive
- Gensim - Gensim Word2Vec implementation
TOP QUESTIONS
Why choose Skip-Gram over CBOW?
Skip-gram models rare words better by predicting many contexts per word. CBOW is faster but can smooth rare word signals.
Category: Difference
What is negative sampling in Skip-Gram?
It trains with a logistic loss against sampled negative words, dramatically reducing computation versus full softmax.
Category: Technique
How to select window size for Skip-Gram?
Smaller windows capture syntactic relations, larger windows capture semantics. Tune based on task and corpus size.
Category: HowTo
What are common mistakes with Skip-Gram?
Insufficient corpus cleaning, poor tokenization, and ignoring subword models. Ensure consistent preprocessing and evaluate with intrinsic tasks.
Category: Mistakes
What risks exist using Skip-Gram embeddings?
Biases in text corpora propagate into embeddings. Audit with fairness metrics and consider debiasing techniques.
Category: Risk
Is Skip-Gram still relevant in modern NLP?
Yes for lightweight tasks and interpretability, though transformer-based contextual embeddings dominate many applications.
Category: Relevancy
How to evaluate Skip-Gram embeddings?
Use analogy tests, similarity benchmarks, and downstream task performance. Visualize with t-SNE/UMAP for qualitative checks.
Category: Testing
Can Skip-Gram be combined with subword models?
Yes, fastText adds character n-grams, improving morphology and OOV performance.
Category: Technique
What corpus size is needed for Skip-Gram?
Larger corpora improve quality and coverage; millions of tokens or more are typical for robust embeddings.
Category: Requirements