The Data Exchange Podcast: Weifeng Zhong on bringing NLP and machine learning to economic policy studies.
Subscribe: iTunes, Android, Spotify, Stitcher, Google, and RSS.
In this episode of the Data Exchange I speak with Weifeng Zhong, Senior Research Fellow at the Mercatus Center at George Mason University. He is the core maintainer of the open source Policy Change Index (PCI), a framework that uses machine learning and NLP to “process and read” large amounts of text to discern government priorities and policies. The initial PCI is focused on major policy shifts in China and uses NLP and machine learning to process and analyze the People’s Daily.
More broadly, Weifeng’s work focuses on bringing NLP and machine learning to economic policy studies. PCI is a great example of potential applications of ML and NLP to many disciplines in the social science and humanities. As Weifeng pointed out in our conversation, tools now make it possible for non experts to automate text processing and understanding and allow scholars to more efficiently study large corpuses.
Subscribe to our Newsletter:
We also publish a popular newsletter where we share highlights from recent episodes, trends in AI / machine learning / data, and a collection of recommendations.
- Weifeng Zhong will be speaking on October 7th at the NLP Summit, a FREE virtual conference and industry gathering for people interested in applications of natural language technologies.
- Matthew Honnibal: “Building open source developer tools for language applications”
- Lauren Kunze: “How to build state-of-the-art chatbots”
- Alan Nichol: “Best practices for building conversational AI applications”
- Amy Heineike: “Machines for unlocking the deluge of COVID-19 papers, articles, and conversations”
- Denise Gosnell: “How graph technologies are being used to solve complex business problems”
- Edmon Begoli: “Hyperscaling natural language processing”
[Image: National Library of China from Wikimedia]