Answers.Body: <p>N-grams are defined as sets of n contiguous words. We use n-grams because they are more useful than random combinations of words across the sentence. Intuitively, combinations of nearby words have more semantic meaning than combinations of distant words.</p>
  <p>Also, using all possible combinations of n embeddings would take much longer, especially since (1D) convolutions are such efficient operations.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-03-14T21:00:58.380Z
Answers.LastActivityDate: 2020-12-04T10:22:25.773Z
Answers.LastEditDate: 2020-12-04T10:22:25.773Z
Answers.UserDisplayName: Philip Raeisghasem
Body: <p>Suppose we are using word2vec and have embeddings of individual words <span class="math-container">$w_1, \dots, w_{10}$</span>. Let's say we wanted to analyze <span class="math-container">$2$</span> grams or <span class="math-container">$3$</span> grams.</p>
  <p>Why would adding all the possible embeddings, <span class="math-container">$\binom{10}{2}$</span> or <span class="math-container">$\binom{10}{3}$</span>, be &quot;worse&quot; than using 1D-convolutions?</p>
  
Tags: <natural-language-processing><word-embedding><word2vec><1d-convolution><n-gram>
Title: Why would adding all the possible embeddings be "worse" than using 1D-convolutions?
UserDisplayName: aiguy123
ThreadText: # Why would adding all the possible embeddings be "worse" than using 1D-convolutions?
  
  (Asked by: username_0 on 2019-03-14)
  
  Suppose we are using word2vec and have embeddings of individual words $w\_1, \dots, w\_{10}$. Let's say we wanted to analyze $2$ grams or $3$ grams.
  
  Why would adding all the possible embeddings, $\binom{10}{2}$ or $\binom{10}{3}$, be "worse" than using 1D-convolutions?
  
  **Tags:** natural-language-processing, word-embedding, word2vec, 1d-convolution, n-gram
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-03-14. Score: 0
  
  N-grams are defined as sets of n contiguous words. We use n-grams because they are more useful than random combinations of words across the sentence. Intuitively, combinations of nearby words have more semantic meaning than combinations of distant words.
  
  Also, using all possible combinations of n embeddings would take much longer, especially since (1D) convolutions are such efficient operations.
  
  
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