Year: 2026
Type: conference-paper
Abstract: Personally identifiable information (PII or PI) can appear in a wide variety of linguistic data, posing both ethical and legal challenges for conducting research and developing applications involving such texts.In this paper, we investigate the alignment between automatic clustering of FastText and Transformer embedding representations of personal information spans sourced from essays written by adult learners of Swedish as a second language and the general and detailed personal information labels assigned to these spans by expert annotators.Our goals are to assess the extent of overlap between the semantic categories and evaluate the semantic coherence of the human-assigned classes, which may have implications for de-identification procedures.We observe that while contextual embeddings, especially ones from a specialized word-in-context model, produce relatively good clustering results, they only partly map to the human understanding of how to classify personal information.
Authors: Maria Irena Szawerna, Simon Dobnik
Institutions: Språkbanken Text, University of Gothenburg
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Vetenskapsrådet
OpenAlex ID: W7166552069