Natural Language Processing Workshop Series

We especially welcome faculty and staff researchers who don’t normally have training opportunities.

Written text contains a wealth of information that can be turned into research data to study almost every aspect of human behavior, human health and our society. However, converting text to usable data requires an understanding of standard techniques from the field of Natural Language Processing (NLP). MIDAS and the AI Lab jointly organized a monthly series of NLP workshops during the Fall 2022 semester.

This workshop helped researchers to assess whether to incorporate NLP/text data into their research agenda, determine what expertise they might want to seek from collaborators for future projects, and know what skills they wanted to develop further in this area, as well as where to look for additional learning opportunities.

Natural Language Processing Workshop Series

Workshop 3 | NLP Research Project Workflows

November 9, 2022 2:00 pm
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In this afternoon workshop, we will discuss incorporating NLP into broader project workflows and introduce work from some labs at U-M applying NLP to study mental health. Co-Instructors: Prerequisites and software requirements: Knowledge of Python would be helpful, but not required.Registration is required. 

Natural Language Processing Workshop Series

Workshop 2 | What’s in Text Data?

October 10, 2022 3:00 pm
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Text contains rich information about human knowledge, opinions, and communication styles, but how do we extract insight about all this from the data? In this second workshop in the series, we will first introduce Jupyter notebooks, a popular platform for performing data science research. Then we will discuss how to choose a dataset for your … Read more

Natural Language Processing Workshop Series

Workshop 1 | Introduction to Natural Language Processing

September 19, 2022 3:00 pm
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In the first workshop of the series, we will provide a broad overview of NLP and introduce basic concepts used in NLP, including keyword counting, sentiment classification, and topic modeling. Additional topics include how NLP can be used, what the data looks like, what social science questions could be answered using NLP, and more. Lead … Read more

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