NER-tagging examples and visualization
One of spaCy's most impressive offerings is its visualization suites and API, and in particular displaCy
[17]. We discussed this in the previous chapter when visualizing part of speech tags. While it is most impressive in visualizing dependency parsing (which we will see next chapter), it doesn't do a half bad job with entities either.
Fig 6.4 An example from a news excerpt from an Elon Musk article on https://www.wired.com
We can see in the above example that spaCy has caught the entities quite well. Indeed, even the Elon Musk page is marked as an organization, which could be considered an organization. It could be the context of Tesla before it or official pages after it – we cannot be sure. We do have an interesting mistake caught again here, where Twitter is a geopolitical entity. Again, we could let this slide if we are considering that Facebook and Twitter are becoming big enough to be a country! But jokes aside, it is not always easy to deal with...