<p><a href="https://i.sstatic.net/65FHjMDB.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/65FHjMDB.png" alt="enter image description here" /></a></p>
<p>Source: <a href="https://colab.research.google.com/drive/1zUHUF8XFdclxMv0B61UGTwCcA60i-u5H#scrollTo=6b8ihpvKCpcE" rel="nofollow noreferrer">https://colab.research.google.com/drive/1zUHUF8XFdclxMv0B61UGTwCcA60i-u5H#scrollTo=6b8ihpvKCpcE</a></p>
<p>Basically, that's exactly what I thougt, it perfect well. Except it also returned negative similarity that can be said as antonym of the word.</p>

CC BY-SA 4.0
2024-09-05T08:24:25.650Z
2024-09-05T08:24:25.650Z
Muhammad Ikhwan Perwira
<p>Given list of keywords:</p>
<pre><code>gyat
fee
skibidi
expenditure
payment
yapping
</code></pre>
<p>How to retrieve keywords that related with a topic, let's say <code>money</code>.</p>
<p>I expect, it returned <code>[1,3,4]</code> which represent indices of keyword that related with its topic.</p>
<p>I'm thinking to use embedding layers of an LLM, and compare with embedding vector representation of <code>money</code> with <em>Cosine Similarity</em>.</p>

<natural-language-processing><large-language-models><word-embedding><embeddings><vector-semantics>
Find keywords in a list that related with specified topic
Muhammad Ikhwan Perwira
# Find keywords in a list that related with specified topic

(Asked by: username_0 on 2024-09-04)

Given list of keywords:
gyat
fee
skibidi
expenditure
payment
yapping

How to retrieve keywords that related with a topic, let's say money.
I expect, it returned [1,3,4] which represent indices of keyword that related with its topic.
I'm thinking to use embedding layers of an LLM, and compare with embedding vector representation of money with Cosine Similarity.

**Tags:** natural-language-processing, large-language-models, word-embedding, embeddings, vector-semantics


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## 1 answers

### Answer by username_0 on 2024-09-05. Score: 0

[![enter image description here](https://i.sstatic.net/65FHjMDB.png)](https://i.sstatic.net/65FHjMDB.png)

Source: <https://colab.research.google.com/drive/1zUHUF8XFdclxMv0B61UGTwCcA60i-u5H#scrollTo=6b8ihpvKCpcE>

Basically, that's exactly what I thougt, it perfect well. Except it also returned negative similarity that can be said as antonym of the word.


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