<p>After more excessive search I have found the description of <a href="https://stevemorse.org/phonetics/bmpm2.htm" rel="nofollow noreferrer">Phonetic Matching</a> algorithm by Alexander Beider and Stephen Morse which basically does what I need to do on filtering</p>

CC BY-SA 4.0
2022-04-23T13:50:46.350Z
2022-04-23T13:50:46.350Z
PiotrK
<p>I am working on fictional single words (names) generator that have to sound like words from a given sample. I have the generator up and running that gives reasonable words 70% of time. I thought of improving this value, ideally to ~99% (so no manual step is necessary to discard clearly missed words).</p>
<p>I've already attempted <em>fuzzy matching</em> to any word in the initial sample (and rejecting words below given thresholds), and this winded up reasonable answers to 95%-99%, but in my experiments this approach is rejecting too many well-sounding names (even 60-70 rejections per 1 generated name - this removes a lot of entropy from the initial set).</p>
<p>Now, I am thinking of using classification instead to tackle the problem from another angle: using classification to discard the generated name.</p>
<p>The problem I have is I have to vectorize a single word based on its sound for classification. I've already excessively searched the Internet and the only solution I see are for vectorizing <em>texts</em>, not <em>single words</em>.</p>
<p>So, the question here: what kind of algorithm for vectorization of single fictional word based on its sounding have I missed?</p>

<natural-language-processing><reference-request><classification><feature-engineering><vectors>
How can I vectorize fictional single word (not sentence!) for classification?
PiotrK
# How can I vectorize fictional single word (not sentence!) for classification?

(Asked by: username_0 on 2022-04-23)

I am working on fictional single words (names) generator that have to sound like words from a given sample. I have the generator up and running that gives reasonable words 70% of time. I thought of improving this value, ideally to ~99% (so no manual step is necessary to discard clearly missed words).

I've already attempted *fuzzy matching* to any word in the initial sample (and rejecting words below given thresholds), and this winded up reasonable answers to 95%-99%, but in my experiments this approach is rejecting too many well-sounding names (even 60-70 rejections per 1 generated name - this removes a lot of entropy from the initial set).

Now, I am thinking of using classification instead to tackle the problem from another angle: using classification to discard the generated name.

The problem I have is I have to vectorize a single word based on its sound for classification. I've already excessively searched the Internet and the only solution I see are for vectorizing *texts*, not *single words*.

So, the question here: what kind of algorithm for vectorization of single fictional word based on its sounding have I missed?

**Tags:** natural-language-processing, reference-request, classification, feature-engineering, vectors


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## 1 answers

### Answer by username_0 on 2022-04-23. Score: 0 | Accepted answer

After more excessive search I have found the description of [Phonetic Matching](https://stevemorse.org/phonetics/bmpm2.htm) algorithm by Alexander Beider and Stephen Morse which basically does what I need to do on filtering


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