"Strengths and Pitfalls of Large-Scale Text Mining for DH"
In this talk, I will how we can use digital methods to generate sustainable knowledge in the humanities. I will give an overview of the data-intensive research methodology and discuss how methods, results, and data relate to each other and must be evaluated as parts of a whole: there is no such thing as a good method, nor is there a way to know if the results are good, without considering the data. I will discuss results as a window from which we can see our data, and how we can reason about the results of digital methods. Finally, I will present the Change is Key! research program and describe our efforts to connect computational research with research questions from the humanities and social sciences.
Related reading here.
Nina Tahmasebi is an associate professor in Natural Language Processing (NLP) at the University of Gothenburg. She studies lexical semantic change from a computational perspective, developing theory, methods, evaluation techniques, and resources. She also works with text mining and AI for digital humanities, both practically and with regards to epistemological questions relating to a data-intensive research methodology: how text mining can be used to generate stable knowledge in text-based humanities and social sciences. She leads a 6-year research program, Change is Key! that received 33.5 Million SEK in funding for research on computational semantic change for the humanities and social sciences funded by Riksbankens Jubileumsfond, starting in 2022.