24 citations · 49 across the 8 of their papers we have counts for
8 papers
An Improved Method for Class-specific Keyword Extraction: A Case Study in the German Business Registry
Stephen Meisenbacher, Tim Schopf, Weixin Yan +2
The task of is often an important initial step in unsupervised information extraction, forming the basis for tasks such as topic modeling or document…
Privacy Risks of General-Purpose AI Systems: A Foundation for Investigating Practitioner Perspectives
Stephen Meisenbacher, Alexandra Klymenko, Patrick Gage Kelley +3
The rise of powerful AI models, more formally (GPAIS), has led to impressive leaps in performance across a wide range of tasks. At the same ti…
A Collocation-based Method for Addressing Challenges in Word-level Metric Differential Privacy
Stephen Meisenbacher, Maulik Chevli, Florian Matthes
Applications of Differential Privacy (DP) in NLP must distinguish between the syntactic level on which a proposed mechanism operates, often taking the form of …
DP-MLM: Differentially Private Text Rewriting Using Masked Language Models
Stephen Meisenbacher, Maulik Chevli, Juraj Vladika +1
The task of text privatization using Differential Privacy has recently taken the form of , in which an input text is obfuscated via the use of generative (…
A Comparative Analysis of Word-Level Metric Differential Privacy: Benchmarking The Privacy-Utility Trade-off
Stephen Meisenbacher, Nihildev Nandakumar, Alexandra Klymenko +1
The application of Differential Privacy to Natural Language Processing techniques has emerged in relevance in recent years, with an increasing number of studies published in establ…
Transforming Unstructured Text into Data with Context Rule Assisted Machine Learning (CRAML)
Stephen Meisenbacher, Peter Norlander
We describe a method and new no-code software tools enabling domain experts to build custom structured, labeled datasets from the unstructured text of documents and build niche mac…