Answers.Body: <h2>Supervised</h2>
  
  <p>Pros:</p>
  
  <ul>
  <li>highest accuracy</li>
  </ul>
  
  <p>Cons:</p>
  
  <ul>
  <li>need a large human-labeled training set</li>
  <li>brittle (doesn't work well with examples that are in a different genre from the training set)</li>
  </ul>
  
  <h2>Semi-supervised</h2>
  
  <h3>Relation bootstrapping</h3>
  
  <p>Pros:</p>
  
  <ul>
  <li>only requires a small set of labeled data (seed relations)</li>
  </ul>
  
  <p>Cons:</p>
  
  <ul>
  <li>complex iterative process</li>
  </ul>
  
  <h3>Distant supervision</h3>
  
  <p>Pros:</p>
  
  <ul>
  <li>training happens in one go (no iterative process)</li>
  </ul>
  
  <p>Cons:</p>
  
  <ul>
  <li>requires a big database of relations</li>
  </ul>
  
  <h2>Unsupervised</h2>
  
  <p>Pros:</p>
  
  <ul>
  <li>don't need any labeled data</li>
  </ul>
  
  <p>Cons:</p>
  
  <ul>
  <li>need to process a huge quantity of unlabeled data (usually web crawling)</li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-03-25T16:09:54.393Z
Answers.LastActivityDate: 2020-03-28T15:50:20.337Z
Answers.LastEditDate: 2020-03-28T15:50:20.337Z
Answers.UserDisplayName: scribu
Body: <p>I am following the NLP course taught by Dan Jurafsky. In the video lectures <a href="https://www.youtube.com/watch?v=a48vIACEC_w&amp;list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&amp;index=50" rel="nofollow noreferrer">Supervised Relation Extraction</a> and <a href="https://www.youtube.com/watch?v=Jxqx70iX9kc&amp;list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&amp;index=51" rel="nofollow noreferrer">Semi Supervised and Unsupervised Relation Extraction</a> Jurafsky explains supervised, semi-supervised and unsupervised relation extraction.</p>
  
  <p>But what are the pros and cons of every relation extraction method compared with the other two relation extraction methods?</p>
  
Tags: <natural-language-processing><comparison><unsupervised-learning><supervised-learning><semi-supervised-learning>
Title: What are the pros and cons of supervised, semi-supervised and unsupervised relation extraction in NLP?
UserDisplayName: DRV
ThreadText: # What are the pros and cons of supervised, semi-supervised and unsupervised relation extraction in NLP?
  
  (Asked by: username_0 on 2020-03-25)
  
  I am following the NLP course taught by Dan Jurafsky. In the video lectures [Supervised Relation Extraction](https://www.youtube.com/watch?v=a48vIACEC_w&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=50) and [Semi Supervised and Unsupervised Relation Extraction](https://www.youtube.com/watch?v=Jxqx70iX9kc&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=51) Jurafsky explains supervised, semi-supervised and unsupervised relation extraction.
  
  But what are the pros and cons of every relation extraction method compared with the other two relation extraction methods?
  
  **Tags:** natural-language-processing, comparison, unsupervised-learning, supervised-learning, semi-supervised-learning
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-03-25. Score: 1
  
  ## Supervised
  
  Pros:
  
  * highest accuracy
  
  Cons:
  
  * need a large human-labeled training set
  * brittle (doesn't work well with examples that are in a different genre from the training set)
  
  ## Semi-supervised
  
  ### Relation bootstrapping
  
  Pros:
  
  * only requires a small set of labeled data (seed relations)
  
  Cons:
  
  * complex iterative process
  
  ### Distant supervision
  
  Pros:
  
  * training happens in one go (no iterative process)
  
  Cons:
  
  * requires a big database of relations
  
  ## Unsupervised
  
  Pros:
  
  * don't need any labeled data
  
  Cons:
  
  * need to process a huge quantity of unlabeled data (usually web crawling)
  
  
  ---
  
  