Are Embeddings (ML) like word clouds?

[stares at error messages]

So, an embedding for a document would look like a word cloud where the size of the word is proportional to the number of times the word appears in the document. Like "Wallace" only appeared once, so it's really small and "Bobert" appeared 100 times so it's the biggest in the cloud.

Then word embedding are the same thing, but with word similarity. Like cat and dog are similar but different, And Hound and Dog overlap because they are synonyms.

I this right? It some seem a bit schizophrenic when they start talking about making "multidimensional space" and then start talking about path finding algorithms and concordance systems.

 

[We Are The Witches]

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Ah! I see exactly why this feels confusing. You’re intuitively on the right track, but embeddings are not quite like word clouds. Let me break it down clearly:

Word Clouds ≠ Embeddings​

Word clouds:

• Visual tool.
• Word size is proportional to frequency.
• No real “relationship” between words beyond their counts.
• Mostly visual, not computationally useful for ML tasks.

Word embeddings:

• A numerical vector representation of a word (or document).
• Captures semantic meaning, not frequency.
• Words that are similar in meaning are represented by vectors that are close in space.

Think of embeddings as a map of meanings, not a map of counts.

How embeddings really work​

For words:

"cat" → [0.12, -0.43, 0.87, ...] # a vector in 300D space
"dog" → [0.10, -0.45, 0.88, ...]

• cat and dog vectors are close together, because they often appear in similar contexts.
• car is farther away from cat.

For documents:

• Often averaging the word vectors or using more advanced models (like Transformers).
• The document’s embedding captures its overall meaning, not just which words appear.

Multidimensional space​

When people talk about 300D or 768D space:

• Each word/document is a point in this high-dimensional space.
• Distance between points = semantic similarity.

Example:
vec("king") - vec("man") + vec("woman") ≈ vec("queen")

• This is a classic embedding arithmetic trick, showing semantic relationships.

Why it seems schizophrenic​

Because ML folks often mix different ideas in one conversation:

• Embeddings = high-dimensional coordinates
• Concordance / path-finding algorithms = graph-based analysis of text
• Word clouds = visual summary

All of these relate to text analysis, but they live in different worlds:

• Word clouds → visual/frequency
• Embeddings → numerical/semantic
• Graphs/path-finding → relational/structural

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TL;DR Analogy​

• Word cloud: Words sized by count → “what’s frequent?”
• Word embedding: Words placed in space → “what’s similar in meaning?”
• Document embedding: A point summarizing the meaning of the whole document.

Frequency doesn’t matter unless the embedding method uses it (like TF-IDF, which is a simpler embedding).