<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>

CC BY-SA 4.0
2020-03-25T16:09:54.393Z
2020-03-28T15:50:20.337Z
2020-03-28T15:50:20.337Z
scribu
<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>

<natural-language-processing><comparison><unsupervised-learning><supervised-learning><semi-supervised-learning>
What are the pros and cons of supervised, semi-supervised and unsupervised relation extraction in NLP?
DRV
# 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


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## 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)


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