Answers.Body: <p>I think that literature is simply inconsistent in this regard. But here's a distinction that I think helps to shade a bit of light on this question:</p>
  <p><em><strong>text representation</strong></em>: as you said we have to convert text into numerical variables. This term refers to general strategies to convert text into numbers, like embedding, bag of words, and so on.</p>
  <p><em><strong>text features representation</strong></em>: this is <strong>what we feed to a real model</strong>. The difference is that it can be a combination of different text representation. For example, I could apply a TF-IDF vectorizer to a corpus and use it to encode each word of a sentence and then use also pre-trained embedding to encode the same sentence/document, concatenating for each word both vectors. Or another commonly used strategy is to use multiple n-grams, which also consist in applying multiple text representations to the same sentence/document.</p>
  
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Answers.Text: I feel that **what we fed to a real model** is the key point...
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Answers.Text: thanks for the feedback, I edited the answer to stress that
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Body: <p>Text representation, in simple words, is representing text in sensible numeric form. You can read in detail <a href="https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-39940-9_420" rel="nofollow noreferrer">from</a> the following paragraph</p>
  <blockquote>
  <p>Text representation is one of the fundamental problems in text mining
  and Information Retrieval (IR). It aims to <strong>numerically represent the
  unstructured text documents to make them mathematically computable</strong>.
  For a given set of text documents <span class="math-container">$D = \{d_i, i=1, 2,...,n\}$</span>, where
  each <span class="math-container">$d_i$</span> stands for a document, the problem of text representation
  is to represent each <span class="math-container">$d_i$</span> of <span class="math-container">$D$</span> as a point <span class="math-container">$s_i$</span> in a numerical space
  <span class="math-container">$S$</span>, where the distance/similarity between each pair of points in
  space <span class="math-container">$S$</span> is well defined.</p>
  </blockquote>
  <p>But, I came across the phrase &quot;text feature representation&quot; in research papers. Features, in general, are <a href="https://ai.stackexchange.com/questions/30378/can-i-always-interpret-features-as-random-variables-in-machine-learning-safely">present in dataset</a>. But, I think, features can be characters or words or documents or complete text (as a single feature?) in the case of the text. I am not sure about what we call features in the text.</p>
  <p>So, I am not sure about what is meant by <strong>text feature representation</strong>. Is it the same as text representation?</p>
  
Tags: <natural-language-processing><terminology><definitions><vector-semantics>
Title: Is there any difference between the phrases "text representation" and "text feature representation"?
UserDisplayName: hanugm
ThreadText: # Is there any difference between the phrases "text representation" and "text feature representation"?
  
  (Asked by: username_0 on 2021-08-30)
  
  Text representation, in simple words, is representing text in sensible numeric form. You can read in detail [from](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-39940-9_420) the following paragraph
  
  > Text representation is one of the fundamental problems in text mining
  > and Information Retrieval (IR). It aims to **numerically represent the
  > unstructured text documents to make them mathematically computable**.
  > For a given set of text documents $D = \{d\_i, i=1, 2,...,n\}$, where
  > each $d\_i$ stands for a document, the problem of text representation
  > is to represent each $d\_i$ of $D$ as a point $s\_i$ in a numerical space
  > $S$, where the distance/similarity between each pair of points in
  > space $S$ is well defined.
  
  But, I came across the phrase "text feature representation" in research papers. Features, in general, are [present in dataset](https://ai.stackexchange.com/questions/30378/can-i-always-interpret-features-as-random-variables-in-machine-learning-safely). But, I think, features can be characters or words or documents or complete text (as a single feature?) in the case of the text. I am not sure about what we call features in the text.
  
  So, I am not sure about what is meant by **text feature representation**. Is it the same as text representation?
  
  **Tags:** natural-language-processing, terminology, definitions, vector-semantics
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-08-31. Score: 1
  
  I think that literature is simply inconsistent in this regard. But here's a distinction that I think helps to shade a bit of light on this question:
  
  ***text representation***: as you said we have to convert text into numerical variables. This term refers to general strategies to convert text into numbers, like embedding, bag of words, and so on.
  
  ***text features representation***: this is **what we feed to a real model**. The difference is that it can be a combination of different text representation. For example, I could apply a TF-IDF vectorizer to a corpus and use it to encode each word of a sentence and then use also pre-trained embedding to encode the same sentence/document, concatenating for each word both vectors. Or another commonly used strategy is to use multiple n-grams, which also consist in applying multiple text representations to the same sentence/document.
  
  > **Comments:**
  > **username_0**: I feel that **what we fed to a real model** is the key point...  
  > **username_1**: thanks for the feedback, I edited the answer to stress that  
  
  ---
  
  