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In this video, we return to our rules-based method that we saw in Video 02 of this series. Instead of trying to write out the rules ourselves, we are going to write out some functions to generate patterns that account for any and all possible ways in which a name is used in Harry Potter. To do this, we will take advantage of spaCy's EntityRuler, which allows us to create rules within a spaCy NER pipeline. This will allow us in the next video to use these patterns to cultivate a good training set to then in the video after that train a custom spaCy NER model. GitHub link: https://github.com/wjbmattingly/ner_y... If you enjoy this video, please subscribe. I provide all my content at no cost. If you want to support my channel, please donate via PayPal: https://www.paypal.com/cgi-bin/webscr... Patreon: / wjbmattingly (its my www.themedievalworld.com account as well). If there's a specific video you would like to see or a tutorial series, let me know in the comments and I will try and make it. If you liked this video, check out www.PythonHumanities.com, where I have Coding Exercises, Lessons, on-site Python shells where you can experiment with code, and a text version of the material discussed here. You can follow me at: / wjb_mattingly