Explore vector arithmetic, semantic
relationships,
and bias in word embeddings.
functions
Word Vector Arithmetic
Enter words to explore semantic
relationships through vector operations. E.g., King − Man + Woman ≈ Queen
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Nearest Neighbors
info
How It Works
1
Vector Representation
Each word is mapped to a high-dimensional vector that captures its semantic meaning.
2
Arithmetic Operations
Subtracting and adding vectors reveals semantic relationships (e.g., royalty,
gender).
3
Nearest Neighbor Search
We find words whose vectors are closest (by cosine similarity) to the result vector.
Cosine Similarity
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Try These Examples
3D Word Vector Space
PCA projection of word vectors into 3D
space. Hover over points to see words.
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Gender Bias in Profession Embeddings
Cosine distance between profession words
and gender-associated terms. Higher values (toward "he") indicate male bias; lower values
(toward "she") indicate female bias.
Configuration
Bias Summary
Most Male-Biased—
Most Female-Biased—
Most Neutral—
Mean Bias Score—
science
WEAT Score (Caliskan 2017)
Click "Run Debiasing Demo" to compute WEAT scores.
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Learning Point
The WEAT (Word Embedding Association Test) by Caliskan et al. (2017)
quantifies bias using an effect size (d) analogous to the IAT.
Hard Debiasing (Bolukbasi et al., 2016) projects embeddings onto the
gender subspace and removes the component along the bias direction.
Click "Run Debiasing Demo" to see before/after WEAT scores and how the bias chart changes.
memory
Loading GloVe Embeddings
Real Pre-trained Vectors
• Four model sizes
LOADING
Loading GloVe 6B pre-trained word vectors (100d, ~20,000
words).
All computation is client-side. No data is sent to any server.