Answers.Body: <p>In the end I embedded each text individually with a shared embedding layer and passed these embeddings on to the latter stages of the neural network.  This is similar to existing work, e.g. <a href="https://github.com/ourresearch/openalex-concept-tagging" rel="nofollow noreferrer">openalex</a>, and performed slightly better than concatenating the inputs.</p>
  
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Answers.CreationDate: 2024-10-25T06:38:14.117Z
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Body: <p>What is the best setup for a neural network if I want to classify records containing multiple text fields?  For example, a record has a title and an abstract:</p>
  <pre><code>title:    Neural Networks for Pattern Recognition
  abstract: This book provides the first comprehensive treatment of feed-forward neural networks [...]
  </code></pre>
  <p>I've seen other people concatenating all text fields (with or without boundary symbols) before the feed it to the classifier.  So in the example, the input to the classifier might become just the single text</p>
  <pre><code>Neural Networks for Pattern Recognition &lt;B&gt; This book provides the first comprehensive treatment of feed-forward neural networks [...]
  </code></pre>
  <p>Are there better ways to handle this situation of having multiple text inputs?</p>
  
Tags: <neural-networks><text-classification>
Title: Multiple text inputs to neural network
UserDisplayName: raywib
ThreadText: # Multiple text inputs to neural network
  
  (Asked by: username_0 on 2023-11-01)
  
  What is the best setup for a neural network if I want to classify records containing multiple text fields?  For example, a record has a title and an abstract:
  title:    Neural Networks for Pattern Recognition
  abstract: This book provides the first comprehensive treatment of feed-forward neural networks [...]
  
  I've seen other people concatenating all text fields (with or without boundary symbols) before the feed it to the classifier.  So in the example, the input to the classifier might become just the single text
  Neural Networks for Pattern Recognition  This book provides the first comprehensive treatment of feed-forward neural networks [...]
  
  Are there better ways to handle this situation of having multiple text inputs?
  
  **Tags:** neural-networks, text-classification
  
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  ## 1 answers
  
  ### Answer by username_0 on 2024-10-25. Score: 0 | Accepted answer
  
  In the end I embedded each text individually with a shared embedding layer and passed these embeddings on to the latter stages of the neural network. This is similar to existing work, e.g. [openalex](https://github.com/ourresearch/openalex-concept-tagging), and performed slightly better than concatenating the inputs.
  
  
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