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        <title>De-identification of Privacy-related Entities in Job Postings</title>
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        <namePart type="given">Kristian</namePart>
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    <abstract>De-identification is the task of detecting privacy-related entities in text, such as person names, emails and contact data. It has been well-studied within the medical domain. The need for de-identification technology is increasing, as privacy-preserving data handling is in high demand in many domains. In this paper, we focus on job postings. We present JobStack, a new corpus for de-identification of personal data in job vacancies on Stackoverflow. We introduce baselines, comparing Long-Short Term Memory (LSTM) and Transformer models. To improve these baselines, we experiment with BERT representations, and distantly related auxiliary data via multi-task learning. Our results show that auxiliary data helps to improve de-identification performance. While BERT representations improve performance, surprisingly “vanilla” BERT turned out to be more effective than BERT trained on Stackoverflow-related data.</abstract>
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%0 Conference Proceedings
%T De-identification of Privacy-related Entities in Job Postings
%A Jensen, Kristian Nørgaard
%A Zhang, Mike
%A Plank, Barbara
%Y Dobnik, Simon
%Y Øvrelid, Lilja
%S Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)
%D 2021
%8 may 31–2 jun
%I Linköping University Electronic Press, Sweden
%C Reykjavik, Iceland (Online)
%F jensen-etal-2021-de
%X De-identification is the task of detecting privacy-related entities in text, such as person names, emails and contact data. It has been well-studied within the medical domain. The need for de-identification technology is increasing, as privacy-preserving data handling is in high demand in many domains. In this paper, we focus on job postings. We present JobStack, a new corpus for de-identification of personal data in job vacancies on Stackoverflow. We introduce baselines, comparing Long-Short Term Memory (LSTM) and Transformer models. To improve these baselines, we experiment with BERT representations, and distantly related auxiliary data via multi-task learning. Our results show that auxiliary data helps to improve de-identification performance. While BERT representations improve performance, surprisingly “vanilla” BERT turned out to be more effective than BERT trained on Stackoverflow-related data.
%U https://aclanthology.org/2021.nodalida-main.21
%P 210-221

Markdown (Informal)

[De-identification of Privacy-related Entities in Job Postings](https://aclanthology.org/2021.nodalida-main.21) (Jensen et al., NoDaLiDa 2021)

• De-identification of Privacy-related Entities in Job Postings (Jensen et al., NoDaLiDa 2021)

ACL

• Kristian Nørgaard Jensen, Mike Zhang, and Barbara Plank. 2021. De-identification of Privacy-related Entities in Job Postings. In Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa), pages 210–221, Reykjavik, Iceland (Online). Linköping University Electronic Press, Sweden.