Demo: Math on Words

The reading compressed four points in the plane down to one number. Here we use GloVe embeddings trained by weighted least squares on log word co-occurrence counts from a large text corpus, without human-provided labels.

Each of \(400{,}000\) words is represented by a point in \(\mathbb{R}^{50}\). We examine its nearest neighbors and difference vectors.

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

A word’s nearest neighbors by cosine similarity show which relationships the embedding preserves.

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 with word vectors

Take the embedding of “king,” subtract “man,” add “woman,” and find the stored word vector nearest to the result. The result itself is not a stored word vector.

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

The training data contains no explicit labels for “gender” or “royalty”; these directions arise from patterns in word co-occurrence.

Project the relation to 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

Test other analogy relations

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

These GloVe vectors contain approximate directions for some relations, including gender and capital-of, that support vector analogies. Other relations fail, so this structure is an observed property of the embeddings rather than a guarantee of the objective.