About me

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Stefania Degaetano-Ortlieb

Welcome! I am a Privatdozent/Assistant Professor (Akademische Rätin) at the Department of Language Science and Technology at Saarland University and a board member of the ACL Special Interest Group on Language Technologies for the Socio-Economic Sciences and Humanities (SIGHUM). My research interests lie in text mining and data analytics for research questions from sociolinguistics, register/language variation as well as change in language use. I’m particularly interested in variation of language use considering linguistic as well as other possible variables that might be at play by using probabilistic models.

Current projects I lead:

I’m a Principal Investigator with Elke Teich in the Collaborative Research Center (SFB 1102) Information Density and Linguistic Encoding for Project B1 on Information Density in Englisch Scientific Writing: A Diachronic Perspective, where we investigate register formation and linguistic densification in the evolution of scientific writing in English (17th century to present).

I’ve recently received funding (April 2023 – March 2024) from the Data-Pin project „Innovative use of AI in education“ for a project on „Overcoming the computational hurdle and exploiting opportunities for humanities students in linguistic research for the Digital Humanities (DH) with Chat-GPT“.

Soon I’ll also advertise two PhD positions within a EU Horizon MSCA on Computational Analysis of Semantic Change Across Different Environments (CASCADE).

Previous projects:

I received funding from Saarland University (2021-2022) with my colleague Francesca Delogu to work on Impact of register and sociolinguistic factors on textual coherence. We combine corpus-based and experimental approaches to investigate how social factors (e.g. expert knowledge in a domain) may affect comprehension. We assume that the use of cohesive devices in a text highly depends on (a) a text’s function in a situational context, i.e. the register (e.g. narrative vs expository texts), and (b) social factors, e.g. the comprehender’s age, education or expertise in a particular domain.

Previously, I had received UdS funding to investigate Linguistic Profiles of Social Variables in Diachrony (SLingPro). To observe possible linguistic profiles of social variables in diachrony my team and I have used the Old Bailey Corpus (Huber et al., 2016) — a digital collection of spoken texts based on the proceedings of the London’s central criminal court from the 18th and 19th century, which is annotated for social variables such as age, gender, and social class.

In my PhD I focused on combining macro- and micro-analytical methods for register analysis on evaluative language (PhD Thesis).