@inproceedings{szawerna-dobnik-2026-birds,
title = "Birds of a Feather: Do Embedding Representations of Personal Information Flock Together?",
author = "Szawerna, Maria Irena and
Dobnik, Simon",
editor = {Siegert, Ingo and
Szawerna, Maria Irena and
Choukri, Khalid and
Dobnik, Simon and
Kamocki, Pawe{\l} and
Lindstr{\"o}m Tiedemann, Therese and
Lison, Pierre and
Mu{\~n}oz S{\'a}nchez, Ricardo and
Pil{\'a}n, Ildik{\'o} and
S{\"o}derg{\r{a}}rd, Lisa and
Talmoudi, Kossay and
Volodina, Elena and
Vu, Xuan-Son},
booktitle = "Proceedings of the Joint Workshop on Legal and Ethical Issues in Human Language Technologies and Computational Approaches to Language Data Pseudonymization, Anonymization, De-identification, and Data Privacy ({LEGAL}2026 and {CALD}-pseudo 2026) @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA",
url = "https://aclanthology.org/2026.legal-1.7/",
doi = "10.63317/4ohwx42xhvzy",
pages = "62--72",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Birds of a Feather: Do Embedding Representations of Personal Information Flock Together?
%A Szawerna, Maria Irena
%A Dobnik, Simon
%Y Siegert, Ingo
%Y Szawerna, Maria Irena
%Y Choukri, Khalid
%Y Dobnik, Simon
%Y Kamocki, Paweł
%Y Lindström Tiedemann, Therese
%Y Lison, Pierre
%Y Muñoz Sánchez, Ricardo
%Y Pilán, Ildikó
%Y Södergård, Lisa
%Y Talmoudi, Kossay
%Y Volodina, Elena
%Y Vu, Xuan-Son
%S Proceedings of the Joint Workshop on Legal and Ethical Issues in Human Language Technologies and Computational Approaches to Language Data Pseudonymization, Anonymization, De-identification, and Data Privacy (LEGAL2026 and CALD-pseudo 2026) @ LREC 2026
%D 2026
%8 May
%I ELRA
%C Palma, Mallorca (Spain)
%F szawerna-dobnik-2026-birds
%X 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.
%R 10.63317/4ohwx42xhvzy
%U https://aclanthology.org/2026.legal-1.7/
%U https://doi.org/10.63317/4ohwx42xhvzy
%P 62-72
Markdown (Informal)
[Birds of a Feather: Do Embedding Representations of Personal Information Flock Together?](https://aclanthology.org/2026.legal-1.7/) (Szawerna & Dobnik, LEGAL-CALD-pseudo 2026)
ACL