Michael Brenndoerfer | Data & AI, Private Equity, Technology

Michael Brenndoerfer

The Distributional Hypothesis › Visualizing Distributional Similarity

Michael BrenndoerferMarch 29, 20253 min read

Language AI HandbookAll chapters

Part 3 of 67Distributional Semantics

Chapter 15The Distributional Hypothesis › Visualizing Distributional Similarity

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Let's visualize the distributional structure we've computed. A 2D projection makes the similarity structure intuitive.

Out[13]:

Visualization

2D scatter plot of word vectors projected with PCA, showing semantic clusters by color. — 2D PCA projection of distributional word vectors for selected content words. Animals (cat, dog), fruits (apple, banana), and motion verbs (run, walk) form distinct clusters, emerging from co-occurrence patterns alone with no explicit semantic labels.

The PCA projection shows the distributional structure. Animals ("cat," "dog") cluster together, as do fruits ("apple," "banana") and motion verbs ("run," "walk"). These groupings emerge from no source other than co-occurrence patterns in our small corpus. This is a scaled-down but faithful demonstration of what large-scale distributional methods achieve.

The fact that PCA, a purely linear transformation, can separate these semantic groups is significant. It means the distributional signal is strong enough to be captured in the first two principal components of variance, the directions along which the word vectors differ most from one another. In a real corpus with millions of sentences, this structure becomes even cleaner.