Pretrained named entity recognition (NER) models are useful to extract structured information from text data. For example, NER models allow you to find people, organizations, locations, and dates in text. While NER models are widely available, using them off-the-shelf is often not enough in a research context. This workshop introduces NER, how to use NER models with spaCy (a widely used Python library), the possibilities and limitations of using NER models off-the-shelf, and how to customize spaCy’s NER models for your research project.
Prerequisite: Participants should be familiar with Python at the level of the Python Fundamentals workshop, another introductory Python workshop, or be a self-taught Python coder. Previous familiarity with named entity recognition or machine learning is not required.
Leticia Vega
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