Demo: Math on Words

The reading compressed four points in the plane down to one number. Here we look at the other extreme: GloVe embeddings, trained with exactly the self-supervised recipe from the reading (predict a word from the words around it) on a huge corpus of text, with no human-provided labels.

Each of \(400{,}000\) words ends up as a single point in \(\mathbb{R}^{50}\). Today we ask what that geometry actually looks like.

import numpy as np
import matplotlib.pyplot as plt
import scienceplots
import gensim.downloader as api

plt.style.use(["science", "no-latex"])
TEAL, CARDINAL = "#009090", "#9c1b33"

model = api.load("glove-wiki-gigaword-50")
print(f"vocabulary size: {len(model.index_to_key)}, embedding dimension: {model.vector_size}")

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vocabulary size: 400000, embedding dimension: 50

Nearest neighbors

If the embedding is any good, a word’s nearest neighbors by cosine similarity should be words a person would call related.

for query in ["king", "computer", "sad"]:
    neighbors = model.most_similar(query, topn=5)
    print(f"{query}:")
    for word, sim in neighbors:
        print(f"    {word:12s} cosine similarity {sim:.3f}")

king:
    prince       cosine similarity 0.824
    queen        cosine similarity 0.784
    ii           cosine similarity 0.775
    emperor      cosine similarity 0.774
    son          cosine similarity 0.767
computer:
    computers    cosine similarity 0.917
    software     cosine similarity 0.881
    technology   cosine similarity 0.853
    electronic   cosine similarity 0.813
    internet     cosine similarity 0.806
sad:
    awful        cosine similarity 0.810
    sorry        cosine similarity 0.788
    terrible     cosine similarity 0.772
    sadly        cosine similarity 0.769
    feeling      cosine similarity 0.767

Arithmetic on meaning

Now the example from the reading: take the embedding of “king,” subtract “man,” add “woman,” and look for the nearest word to the result, which is a vector that corresponds to no word at all.

nearest = model.most_similar(positive=["king", "woman"], negative=["man"], topn=5)
print('"king" - "man" + "woman" is closest to:')
for word, sim in nearest:
    print(f"    {word:12s} cosine similarity {sim:.3f}")

"king" - "man" + "woman" is closest to:
    queen        cosine similarity 0.852
    throne       cosine similarity 0.766
    prince       cosine similarity 0.759
    daughter     cosine similarity 0.747
    elizabeth    cosine similarity 0.746

No word was ever labelled with “gender” or “royalty.” The direction emerged purely from which words tend to appear near which other words.

The geometry, in two dimensions

Project a handful of related word pairs onto their top two principal components (today’s PCA, applied to \(50\)-dimensional word vectors instead of the reading’s \(2\)-dimensional toy points) and see whether “gender” is really a consistent direction. If it is, the two colors should separate along a single axis.

words = ["king", "queen", "man", "woman", "prince", "princess",
         "uncle", "aunt", "father", "mother", "boy", "girl"]
is_male = np.array([True, False, True, False, True, False,
                    True, False, True, False, True, False])

X = np.array([model[w] for w in words])
Xc = X - X.mean(axis=0)                       # center, exactly as the reading does
u, s, vt = np.linalg.svd(Xc, full_matrices=False)
proj = Xc @ vt[:2].T                          # coordinates along the top two components

explained = (s[:2] ** 2).sum() / (s ** 2).sum()
print(f"variance explained by the top two components: {explained:.2f}")

fig, ax = plt.subplots(figsize=(7, 5))
ax.scatter(proj[is_male, 0], proj[is_male, 1], color=TEAL, s=45, zorder=3,
           label="Masculine word")
ax.scatter(proj[~is_male, 0], proj[~is_male, 1], color=CARDINAL, s=45, zorder=3,
           label="Feminine word")
for (x, y), word in zip(proj, words):
    ax.annotate(word, (x, y), textcoords="offset points", xytext=(5, 4), fontsize=9)
ax.margins(x=0.16, y=0.10)
ax.set_xlabel("Top principal component")
ax.set_ylabel("Second principal component")
ax.legend(frameon=False, loc="lower right")
plt.show()

variance explained by the top two components: 0.66

What if we tried a different analogy?

Swap in a different relationship below (capital cities, verb tenses, comparative and superlative adjectives) and see whether the same arithmetic still works. It does not always.

a, b, c = "paris", "france", "japan"    # change me! (a is to b as ? is to c)

nearest = model.most_similar(positive=[a, c], negative=[b], topn=5)
print(f'"{a}" - "{b}" + "{c}" is closest to:')
for word, sim in nearest:
    print(f"    {word:12s} cosine similarity {sim:.3f}")

"paris" - "france" + "japan" is closest to:
    tokyo        cosine similarity 0.923
    shanghai     cosine similarity 0.837
    japanese     cosine similarity 0.790
    osaka        cosine similarity 0.783
    beijing      cosine similarity 0.748

Punchline: an embedding trained on nothing but “predict a word from its neighbors” ends up with directions that line up with human categories (gender, capital-of) closely enough to support literal vector arithmetic, and self-supervision alone produced all of it.