Michael Brenndoerfer | Data & AI, Private Equity, Technology

Michael Brenndoerfer

Negative Sampling › The Core Insight: Binary Classification Instead of Softmax

Michael BrenndoerferApril 4, 20252 min read

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Chapter 21Negative Sampling › The Core Insight: Binary Classification Instead of Softmax

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The full Skip-gram model answers: "What is the probability of each vocabulary word being a context word?" This requires computing a probability distribution over 100,000+ words. Negative sampling asks a simpler question: "Is this specific word pair a real context pair or a fake one?" This is binary classification, requiring only a single probability per pair.

Negative Sampling

Negative sampling approximates the full softmax objective by converting the multi-class classification problem into multiple binary classification problems. For each positive (center, context) pair from the corpus, we sample $k$ negative pairs and train the model to distinguish real pairs from fake ones.

Consider the sentence "The quick brown fox jumps." When "fox" is the center word, "brown" is a observed context word. But "elephant" or "democracy" don't appear near "fox" in this context. They're negatives, and we can use them to train the model.

In[4]:

Code

# A genuine training pair from our corpus
center_word = "fox"
positive_context = "brown"  # Actually appears near "fox"

# Negative samples: words that don't appear near "fox" in this context
negative_contexts = [
    "elephant",
    "democracy",
    "refrigerator",
    "quantum",
    "bicycle",
]

Out[5]:

Console

Negative Sampling Training Example:
--------------------------------------------------
Center word: 'fox'
Positive context (from corpus): 'brown' -> label: 1

Negative contexts (sampled randomly):
  'elephant' -> label: 0
  'democracy' -> label: 0
  'refrigerator' -> label: 0
  'quantum' -> label: 0
  'bicycle' -> label: 0

Training task: distinguish real pair from fake pairs

The model learns by maximizing the probability for the positive pair while minimizing it for the negative pairs. Through millions of such updates, the embedding space organizes so that words appearing in similar contexts end up nearby.