Answers.Body: <p><a href="https://machinelearningmastery.com/why-one-hot-encode-data-in-machine-learning/" rel="nofollow noreferrer">One-hot encoding</a> is different than the concept of a word embedding, although both approaches use vectors to represent the objects (e.g. words).</p>
  <p>A <strong>one-hot vector</strong> contains one element that is 1 and all other elements are 0. So, for example, the vector <span class="math-container">$[0, 0, 1, 0]$</span> is a one-hot vector, while the vector <span class="math-container">$[0, 2, 0.2, 0]$</span> is not. (Given that the sum of all elements in the one-hot vector is equal to 1 and all elements are in the range <span class="math-container">$[0, 1]$</span>, a one-hot vector is a <a href="https://en.wikipedia.org/wiki/Probability_vector" rel="nofollow noreferrer">probability vector</a>, although this may be irrelevant.) To represent your objects as one-hot vectors, you first need to know how many objects you have (in the context of NLP, this is often the <em>vocabulary size</em>). Let's say you have <span class="math-container">$N$</span> distinct elements
  (e.g. words), then, in order for two objects not to be mapped to the same one-hot vector, you need to have one-hot vectors of <span class="math-container">$N$</span> dimensions. So, if <span class="math-container">$N$</span> is very large, this can be a disadvantage of one-hot encoding, although, in principle, you only need to store the index of the <span class="math-container">$1$</span> for each one-hot vector.</p>
  <p>As opposed to one-hot vectors, which do not encode the meaning of the objects they represent, <strong>word embeddings</strong> <em>semantically</em> represent the objects, i.e. similar objects (for some notion of similarity) are typically mapped to similar word embeddings. Here's a picture that illustrates this concept (taken from the book that I cite below).</p>
  <p><a href="https://i.sstatic.net/v1Q26.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/v1Q26.png" alt="enter image description here" /></a></p>
  <p>Moreover, word embeddings can easily be smaller than one-hot vectors and do not necessarily contain only 0s and 1s. More importantly, word embeddings are derived from data (often learned).</p>
  <p>A similar argument can be said for integer encoding, i.e. they are not learned from data and they do not carry any meaning. <a href="https://web.stanford.edu/%7Ejurafsky/slp3/ed3book.pdf#page=104" rel="nofollow noreferrer">Chapter 6</a> of <a href="https://web.stanford.edu/%7Ejurafsky/slp3/ed3book.pdf" rel="nofollow noreferrer">this book</a> discusses word embeddings more in detail.</p>
  <p>To conclude, the common definition of a word embedding usually implies that word embeddings semantically represent objects and are derived/learned from data, so, generally, one-hot encoding is not a form of word embedding, but it's a way to <a href="https://ai.stackexchange.com/a/20646/2444">embed objects into a vector space</a>.</p>
  
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Answers.Text: Is there any reason that one-hot encoding is preferred over integer encoding for learning these word embeddings?
Answers.UserDisplayName: Antonios Sarikas
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Body: <p>Word embedding refers to the techniques in which a word is represented by a vector. There are also <em>integer encoding</em> and <em>one-hot encoding</em>, which I will collectively call <a href="https://datascience.stackexchange.com/questions/tagged/categorical-encoding"><em>categorical encoding</em></a>.</p>
  <p>I can see no fundamental difference between the categorical encoding and word embedding at a fundamental level. They may be different at an application level.</p>
  <p>Is it true that categorical encoding is a type of word embedding? And are different names solely due to the task in which apply the technique?</p>
  
Tags: <comparison><terminology><word-embedding><categorical-data><one-hot-encoding>
Title: Is categorical encoding a type of word embedding?
UserDisplayName: hanugm
ThreadText: # Is categorical encoding a type of word embedding?
  
  (Asked by: username_0 on 2021-06-04)
  
  Word embedding refers to the techniques in which a word is represented by a vector. There are also *integer encoding* and *one-hot encoding*, which I will collectively call [*categorical encoding*](https://datascience.stackexchange.com/questions/tagged/categorical-encoding).
  
  I can see no fundamental difference between the categorical encoding and word embedding at a fundamental level. They may be different at an application level.
  
  Is it true that categorical encoding is a type of word embedding? And are different names solely due to the task in which apply the technique?
  
  **Tags:** comparison, terminology, word-embedding, categorical-data, one-hot-encoding
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-06-04. Score: 2 | Accepted answer
  
  [One-hot encoding](https://machinelearningmastery.com/why-one-hot-encode-data-in-machine-learning/) is different than the concept of a word embedding, although both approaches use vectors to represent the objects (e.g. words).
  
  A **one-hot vector** contains one element that is 1 and all other elements are 0. So, for example, the vector $[0, 0, 1, 0]$ is a one-hot vector, while the vector $[0, 2, 0.2, 0]$ is not. (Given that the sum of all elements in the one-hot vector is equal to 1 and all elements are in the range $[0, 1]$, a one-hot vector is a [probability vector](https://en.wikipedia.org/wiki/Probability_vector), although this may be irrelevant.) To represent your objects as one-hot vectors, you first need to know how many objects you have (in the context of NLP, this is often the *vocabulary size*). Let's say you have $N$ distinct elements
  (e.g. words), then, in order for two objects not to be mapped to the same one-hot vector, you need to have one-hot vectors of $N$ dimensions. So, if $N$ is very large, this can be a disadvantage of one-hot encoding, although, in principle, you only need to store the index of the $1$ for each one-hot vector.
  
  As opposed to one-hot vectors, which do not encode the meaning of the objects they represent, **word embeddings** *semantically* represent the objects, i.e. similar objects (for some notion of similarity) are typically mapped to similar word embeddings. Here's a picture that illustrates this concept (taken from the book that I cite below).
  
  [![enter image description here](https://i.sstatic.net/v1Q26.png)](https://i.sstatic.net/v1Q26.png)
  
  Moreover, word embeddings can easily be smaller than one-hot vectors and do not necessarily contain only 0s and 1s. More importantly, word embeddings are derived from data (often learned).
  
  A similar argument can be said for integer encoding, i.e. they are not learned from data and they do not carry any meaning. [Chapter 6](https://web.stanford.edu/%7Ejurafsky/slp3/ed3book.pdf#page=104) of [this book](https://web.stanford.edu/%7Ejurafsky/slp3/ed3book.pdf) discusses word embeddings more in detail.
  
  To conclude, the common definition of a word embedding usually implies that word embeddings semantically represent objects and are derived/learned from data, so, generally, one-hot encoding is not a form of word embedding, but it's a way to [embed objects into a vector space](https://ai.stackexchange.com/a/20646/2444).
  
  > **Comments:**
  > **username_2**: Is there any reason that one-hot encoding is preferred over integer encoding for learning these word embeddings?  
  
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