NLP Tutorial 7 - Combining NLP Models and Custom Rules in SpaCy Python Tutorial

In this article, we are going to create some custom rules for our requirements and will add that to our pipeline like expanding named entities and identifying a person’s organization name from a given text. For example: For example, the corpus spaCy’s English models were trained on defining a PERSON entity as just the person name, without titles like “Mr” or “Dr”. This makes sense because it makes it easier to resolve the entity type back to a knowledge base. But what if your application needs the full names, including the titles?

🔊 Watch till last for a detailed description
02:07 Combining NLP models and custom roles
04:06 Expanding named entities
16:11 Use of POS and dependency parsing
27:29 Modify model

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[ijeffking]

Thank you very much. The topics and subjects that you choose to make tutorials are refreshingly appropriate. Keep up the good work!

[ArunSingh-bemw]

If there are 2 or 3 persons involved with different organization and also sentenced is big then what we can do, ..?

[helenserere]

Great video thank you so much. I am looking to add custom rules to distinguish between users' previous, current and future locations in Tweets i.e. I was in London vs I am in London. I hope I will succeed
😃

[techspoc]

AttributeError: 'str' object has no attribute 'ents' . I am getting error

[sabkabaaphu]

at 15:30, why is it not showing Google as 'ORG'?