Answers.Body: <p>While your question has some ambiguities, I try to answer.</p>
  <p>From my understanding you want your model to predict the “topic” of a sentence or a description. It’s just a classification problem with a huge possible number of output classes.</p>
  <p>The first initial issue is a very short length of documents (sentences). Most of the topic modeling algorithms such as LDA have a statistical approach and do not work very well with very short documents (less than 50 words could be a good definition of a very short document).</p>
  <p>The second issue is how do you want to collect enough data to train your model that is supposed to predict the target out of an extremely large number of output classes? Dictionaries are not enough because they offer a single definition for each word. Examples of words in dictionaries don’t help much and they will probably affect your model adversely. How can your model be generalized by a single (or few) example(s) for each class?</p>
  <p>So, it’s not possible, but maybe having some innovations can help.</p>
  <p>Here is the definition of “apple” in the oxford dictionary: ”a round fruit with shiny red or green skin that is fairly hard and white inside”. There are just two nouns in the definition: &quot;fruit&quot; and &quot;Skin&quot;, if we just read the definition without considering these two words, even we, as humans, struggle to guess.</p>
  <p>Consider nouns in input data and use them to build up a natural graph. You just consider main classes such as &quot;fruit&quot;. If you’re getting some good results, consider other words, adj, adv, ...</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-08-01T05:25:17.583Z
Answers.Text: Your point of human struggle to decipher word from the dictionary without knowing the word is a very good point. thanks.
Answers.UserDisplayName: Sid
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-07-30T21:52:41.230Z
Answers.LastActivityDate: 2021-12-22T23:11:40.057Z
Answers.LastEditDate: 2021-12-22T23:11:40.057Z
Answers.UserDisplayName: pedrum
Body: <p>What I mean to say is</p>
  <ol>
  <li>For example, if I give the meaning of Apple from the dictionary as input to the program, it should give output as Apple.</li>
  <li>Or I say My day to day job involves monitoring and managing the resources - the output should be Project management.</li>
  </ol>
  <p>The meaning and the word could be a dictionary or it could be custom. I am looking for ideas and tools to go further on this.</p>
  
Tags: <natural-language-processing><training><python><natural-language-understanding>
Title: How to predict the "word" based on the meaning in a document?
UserDisplayName: Sid
ThreadText: # How to predict the "word" based on the meaning in a document?
  
  (Asked by: username_0 on 2020-07-30)
  
  What I mean to say is
  
  1. For example, if I give the meaning of Apple from the dictionary as input to the program, it should give output as Apple.
  2. Or I say My day to day job involves monitoring and managing the resources - the output should be Project management.
  
  The meaning and the word could be a dictionary or it could be custom. I am looking for ideas and tools to go further on this.
  
  **Tags:** natural-language-processing, training, python, natural-language-understanding
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-07-30. Score: 1
  
  While your question has some ambiguities, I try to answer.
  
  From my understanding you want your model to predict the “topic” of a sentence or a description. It’s just a classification problem with a huge possible number of output classes.
  
  The first initial issue is a very short length of documents (sentences). Most of the topic modeling algorithms such as LDA have a statistical approach and do not work very well with very short documents (less than 50 words could be a good definition of a very short document).
  
  The second issue is how do you want to collect enough data to train your model that is supposed to predict the target out of an extremely large number of output classes? Dictionaries are not enough because they offer a single definition for each word. Examples of words in dictionaries don’t help much and they will probably affect your model adversely. How can your model be generalized by a single (or few) example(s) for each class?
  
  So, it’s not possible, but maybe having some innovations can help.
  
  Here is the definition of “apple” in the oxford dictionary: ”a round fruit with shiny red or green skin that is fairly hard and white inside”. There are just two nouns in the definition: "fruit" and "Skin", if we just read the definition without considering these two words, even we, as humans, struggle to guess.
  
  Consider nouns in input data and use them to build up a natural graph. You just consider main classes such as "fruit". If you’re getting some good results, consider other words, adj, adv, ...
  
  > **Comments:**
  > **username_0**: Your point of human struggle to decipher word from the dictionary without knowing the word is a very good point. thanks.  
  
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