Answers.Body: <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>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-04-23T13:50:46.350Z
Answers.LastActivityDate: 2022-04-23T13:50:46.350Z
Answers.UserDisplayName: PiotrK
Body: <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>
  
Tags: <natural-language-processing><reference-request><classification><feature-engineering><vectors>
Title: How can I vectorize fictional single word (not sentence!) for classification?
UserDisplayName: PiotrK
ThreadText: # 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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