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I am following the NLP course taught by Dan Jurafsky. In the video lectures Supervised Relation Extraction and Semi Supervised and Unsupervised Relation Extraction 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?

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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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