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Embeddings are underrated (2024)

(technicalwriting.dev)
484 points jxmorris12 | 1 comments | | HN request time: 0.221s | source
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kaycebasques ◴[] No.43964290[source]
Hello, I wrote this. Thank you for reading!

The post was previously discussed 6 months ago: https://news.ycombinator.com/item?id=42013762

To be clear, when I said "embeddings are underrated" I was only arguing that my fellow technical writers (TWs) were not paying enough attention to a very useful new tool in the TW toolbox. I know that the statement sounds silly to ML practitioners, who very much don't "underrate" embeddings.

I know that the post is light on details regarding how exactly we apply embeddings in TW. I have some projects and other blog posts in the pipeline. Short story long, embeddings are important because they can help us make progress on the 3 intractable challenges of TW: https://technicalwriting.dev/strategy/challenges.html

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sgbeal ◴[] No.43965226[source]
> I know that the post is light on details regarding how exactly we apply embeddings in TW.

More significantly, after having read the first 6 or 8 paragraphs, i still have no clue what an "embedding" is. From the 3rd paragraph:

> Here’s an overview of how you use embeddings and how they work.

But no mention of what they are (unless perhaps it's buried far deeper in the article).

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1. kadushka ◴[] No.43967398[source]
A word embedding is a representation of a word using many numbers, where each numbers represents some property of the word. Usually we do not know what those properties are because the numbers are learned by a model during processing of a large number of texts.