Answers.Body: <p>It depends on the actual neural network model being used but in general NNs won't be looking into individual words. They'd be fed with context (other words) surrounding the subject word. You can find some more info on most basic approach (Word2Vec) here <a href="https://en.wikipedia.org/wiki/Word2vec" rel="nofollow noreferrer">https://en.wikipedia.org/wiki/Word2vec</a></p>
  <p>Also NN models are not being trained to identify meaning. It's very complicated to explain to a NN such a complex concept as &quot;identifying meaning&quot;. It's in general complicated to actually &quot;explain&quot; or &quot;teach&quot; anything to NN. It will regularly identify &quot;shortcuts&quot; that would allow NN to sand-bag on its training. It would often find strategies to trick your loss function.</p>
  <p>It's all about how you craft your training process and your loss function. Typically a word &quot;meaning&quot; would appear (if I may be so bold) as a bypass product from NN doing it's job so good. I might be wrong but it can just be a mere human feeling that NN truly understand concepts. In reality it's more a numbers game, a reflection of something that might feel real yet there's nobody on the other side of the mirror.</p>
  
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Answers.CreationDate: 2022-11-15T03:22:41.910Z
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Body: <p>The word &quot;shot&quot; has several meanings. For example, when one says they shot an animal, it could mean that they shot it with a gun, or even shot it out of a cannon.</p>
  <p>How can NN trained models be used to identify the appropriate meaning of the word shot?</p>
  
Tags: <neural-networks><natural-language-processing>
Title: How do Neural Networks deal with ambiguity of words?
UserDisplayName: donaldgrafton01
ThreadText: # How do Neural Networks deal with ambiguity of words?
  
  (Asked by: username_0 on 2022-11-15)
  
  The word "shot" has several meanings. For example, when one says they shot an animal, it could mean that they shot it with a gun, or even shot it out of a cannon.
  
  How can NN trained models be used to identify the appropriate meaning of the word shot?
  
  **Tags:** neural-networks, natural-language-processing
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2022-11-15. Score: 1
  
  It depends on the actual neural network model being used but in general NNs won't be looking into individual words. They'd be fed with context (other words) surrounding the subject word. You can find some more info on most basic approach (Word2Vec) here <https://en.wikipedia.org/wiki/Word2vec>
  
  Also NN models are not being trained to identify meaning. It's very complicated to explain to a NN such a complex concept as "identifying meaning". It's in general complicated to actually "explain" or "teach" anything to NN. It will regularly identify "shortcuts" that would allow NN to sand-bag on its training. It would often find strategies to trick your loss function.
  
  It's all about how you craft your training process and your loss function. Typically a word "meaning" would appear (if I may be so bold) as a bypass product from NN doing it's job so good. I might be wrong but it can just be a mere human feeling that NN truly understand concepts. In reality it's more a numbers game, a reflection of something that might feel real yet there's nobody on the other side of the mirror.
  
  
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