Join us for a talk by Paulina Garcia Corral on her research into causal natural language processing. This is part of the Data Science Brown Bag series.
Abstract from the speaker:
Political explanation has been under-investigated. At it's core, political explanations is how political actors construct causal arguments to justify political positions or policy decisions, to name two examples. My PhD project attempts to take some steps in this direction, leveraging the power of Large Language Models. I first attempt to classify text as causal or not causal, to identify the main differences between the two types of language. Then I will construct a causal extraction model that can identify the cause and the effect in any given causal sentences. Finally, using these language models, I will analyze political text to compare causal arguments via constructing causal maps across using the fine-tuned LLMs. In this week’s session, I will talk about the first step: the data annotation strategy and the experimental pipeline used to benchmark classifiers.
Bring your own lunch bag! Light pastries and drinks will be available in case you forget to bring it.
—
The Data Science Brown Bag Series is an informal and interactive gathering where participants bring their own brown bag lunch and engage in discussions on research and insights the field of data and computational social science (light pastries and drinks will be available if you forget your lunch bag!).
The series provides a platform for data enthusiasts, researchers, and practitioners to share their experiences, best practices, and emerging methodologies and research in using data science to analyze and understand social and political phenomena. The brown bag talk series is for anyone interested in data science and social science to network, learn, and share ideas in a casual and friendly setting.
