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You're reading from  Natural Language Processing and Computational Linguistics

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Published inJun 2018
Reading LevelBeginner
PublisherPackt
ISBN-139781788838535
Edition1st Edition
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Bhargav Srinivasa-Desikan
Bhargav Srinivasa-Desikan
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Bhargav Srinivasa-Desikan

Bhargav Srinivasa-Desikan is a research engineer working for INRIA in Lille, France. He is a part of the MODAL (Models of Data Analysis and Learning) team, and he works on metric learning, predictor aggregation, and data visualization. He is a regular contributor to the Python open source community, and completed Google Summer of Code in 2016 with Gensim where he implemented Dynamic Topic Models. He is a regular speaker at PyCons and PyDatas across Europe and Asia, and conducts tutorials on text analysis using Python.
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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...

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Natural Language Processing and Computational Linguistics
Published in: Jun 2018Publisher: PacktISBN-13: 9781788838535

Author (1)

author image
Bhargav Srinivasa-Desikan

Bhargav Srinivasa-Desikan is a research engineer working for INRIA in Lille, France. He is a part of the MODAL (Models of Data Analysis and Learning) team, and he works on metric learning, predictor aggregation, and data visualization. He is a regular contributor to the Python open source community, and completed Google Summer of Code in 2016 with Gensim where he implemented Dynamic Topic Models. He is a regular speaker at PyCons and PyDatas across Europe and Asia, and conducts tutorials on text analysis using Python.
Read more about Bhargav Srinivasa-Desikan