Papers by Ulrike Krieg-Holz
“Beste Grüße, Maria Meyer” — Pseudonymization of Privacy-Sensitive Information in Emails (2022.lrec-1)
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| Challenge: | exploding amount of user-generated content has spurred research to deal with documents from various digital communication formats. |
| Approach: | They propose to identify text spans that carry information revealing an individual’s identity and substitute them with synthetically generated surrogates. |
| Outcome: | The proposed model is based on a German-language email corpus and evaluates its training data on pseudonymized data. |
Acquiring a Formality-Informed Lexical Resource for Style Analysis (2021.eacl-main)
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| Challenge: | lexico-statistics analysis of formality levels in written communication has long been dominated by application concerns, such as authorship and plagiarism assignment problems. |
| Approach: | They propose a lexicon with entries ordered by their degree of (in)formality and let crowdworkers assess the enlarged set of lexical items on a continuous informal-formal scale as a gold standard for evaluation. |
| Outcome: | The proposed lexicon is evaluated on a German-language email corpus and is then evaluated by crowdworkers. |
CodE Alltag 2.0 — A Pseudonymized German-Language Email Corpus (2020.lrec-1)
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| Challenge: | unauthorized use of social media content as a data resource is often neglected . data privacy concerns are often overlooked in NLP research . |
| Approach: | They propose an algorithm for the protection of personal data via pseudonymization by automatically recognizing privacy-sensitive stretches of text in UGC. |
| Outcome: | The proposed algorithm protects personal data via pseudonymization on two hitherto non-anonymized German-language email corpora. |
A Question of Style: A Dataset for Analyzing Formality on Different Levels (2023.findings-eacl)
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| Challenge: | Using machine learning, we can produce contextually appropriate language. |
| Approach: | They present a dataset of German sentence-level formality assessed on a continuous informal-formal scale. |
| Outcome: | The proposed dataset compares sentences from a wide range of genres assessed on a continuous informal-formal scale. |