Body: <p>I have lots of text documents structured as</p>
  
  <pre><code>{
  {
      Item1=[
              {a1=1,
               a2=2,
               a3=3},
              {a1=11,
               a2=22,
               a3=33},
              {a1=41,
               a2=52,
               a3=63},
              {a1=19,
               a2=29,
               a3=39}
      ],
      Item2=[
              {a4=1,
               a5=2,
               a6=3},
              {a4=11,
               a5=22,
               a6=33},
              {a4=41,
               a5=52,
               a6=63},
              {a4=19,
               a5=29,
               a6=39}
      ],
  }
  }
  </code></pre>
  
  <p>Now this can be formatted into two csv's as</p>
  
  <p><a href="https://i.sstatic.net/K8b70.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/K8b70.png" alt="Item 1"></a></p>
  
  <p>and</p>
  
  <p><a href="https://i.sstatic.net/vMb5O.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/vMb5O.png" alt="Item 2"></a></p>
  
  <p>I can write regex parser for this but is there a way by which a neural network or deep learning model can be trained for this, which can create these csvs?</p>
  
  <p>The above example has been indented for better visuals, the raw text looks something like</p>
  
  <pre><code>{{Item1=[{a1=1,a2=2,a3=3},{a1=11,a2=22,{a1=41,a2=52,a3=63},{a1=19,a2=29,a3=39}],Item2=[{a4=1,a5=2,a6=3},{a4=11,a5=22,a6=33},{a4=41,a5=52,a6=63},{a4=19,a5=29,a6=39}]}}
  </code></pre>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-01-10T13:29:17.713Z
Comments.Text: Hi and welcome to this community! Why do you wanna use a neural network for this task? If you can solve it exactly (and not approximately) and efficiently, then you don't need neural networks or machine learning.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-01-10T14:24:28.903Z
Comments.Text: Hi @nbro, thanks for the welcome. The structure I mentioned above is just an example. The variations are infinite. Sometime row separators are {, sometime they are [ and sometime custome delimiters like @#@#@. If a model learn to identify inherent structure, the need to write separate parser will end. That's why I want to train a NN model on this.
Comments.UserDisplayName: Sim101011
Tags: <neural-networks><deep-learning><pattern-recognition><structured-data><data-mining>
Title: how to convert one structured data to another without specifying structure
UserDisplayName: Sim101011
ThreadText: # how to convert one structured data to another without specifying structure
  
  (Asked by: username_0 on 2020-01-10)
  
  I have lots of text documents structured as
  {
  {
      Item1=[
              {a1=1,
               a2=2,
               a3=3},
              {a1=11,
               a2=22,
               a3=33},
              {a1=41,
               a2=52,
               a3=63},
              {a1=19,
               a2=29,
               a3=39}
      ],
      Item2=[
              {a4=1,
               a5=2,
               a6=3},
              {a4=11,
               a5=22,
               a6=33},
              {a4=41,
               a5=52,
               a6=63},
              {a4=19,
               a5=29,
               a6=39}
      ],
  }
  }
  
  Now this can be formatted into two csv's as
  
  and
  
  I can write regex parser for this but is there a way by which a neural network or deep learning model can be trained for this, which can create these csvs?
  The above example has been indented for better visuals, the raw text looks something like
  {{Item1=[{a1=1,a2=2,a3=3},{a1=11,a2=22,{a1=41,a2=52,a3=63},{a1=19,a2=29,a3=39}],Item2=[{a4=1,a5=2,a6=3},{a4=11,a5=22,a6=33},{a4=41,a5=52,a6=63},{a4=19,a5=29,a6=39}]}}
  
  **Tags:** neural-networks, deep-learning, pattern-recognition, structured-data, data-mining
  > **Comments:**
  > **username_1**: Hi and welcome to this community! Why do you wanna use a neural network for this task? If you can solve it exactly (and not approximately) and efficiently, then you don't need neural networks or machine learning.  
  > **username_0**: Hi @nbro, thanks for the welcome. The structure I mentioned above is just an example. The variations are infinite. Sometime row separators are {, sometime they are [ and sometime custome delimiters like @#@#@. If a model learn to identify inherent structure, the need to write separate parser will end. That's why I want to train a NN model on this.  
  
  ---
  
  ## 0 answers
  
  