Machine Learning NER with Python and spaCy (NER for DH 03 )

In this video, we take a brief look at machine learning as an alternative to a rules-based approach to named entity recognition (NER). In this video, we do not learn all of the concepts necessary to understand machine learning NER, rather we explore the basics via spaCy, which is a Python library that allows users to cultivate both rules-based and machine learning-based approaches to NER. In this video, I show you how 4 lines of code can have similar results as hundreds of lines of code for the purpose of performing NER. Why is this possible? Because machine learning models can generalize on unseen data.

In the next video, we will look much more closely at spaCy and begin cultivating a domain-specific set of rules to generate training data for a domain-specific machine learning NER model.

GitHub link: https://github.com/wjbmattingly/ner_youtube

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