mlsanityData to 7 Oct 2026

De-identification of Privacy-related Entities in Job Postings

arXiv 2105.11223 ↗First posted 24 May 2021cs.CLcited by 12

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Abstract (arXiv)

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 upon these baselines, we experiment with contextualized embeddings and distantly related auxiliary data via multi-task learning. Our results show that auxiliary data improves de-identification performance. Surprisingly, vanilla BERT turned out to be more effective than a BERT model trained on other portions of Stackoverflow.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 19 of its 63 references that are in the map. First posted on arXiv 24 May 2021.

Builds on

19 of 63 references.

References · 19

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2018Deep contextualized word representationscited by 2,285
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7 more
  1. 2020Code and Named Entity Recognition in StackOverflowcited by 39
  2. 2016Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Losscited by 30
  3. 2016When is multitask learning effective? Semantic sequence prediction under varying data conditionscited by 21
  4. 2019Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Recordscited by 11
  5. 2018A Deep Learning Architecture for De-identification of Patient Notes: Implementation and Evaluationcited by 6
  6. 2020Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Recordscited by 6
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Cited by year

Citing papers per year: 2022: 4; 2023: 2; 2024: 2; 2025: 4.

12 papers.

  1. 2022The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymizationcited by 94
  2. 2022SkillSpan: Hard and Soft Skill Extraction from English Job Postingscited by 41
  3. 2023ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domaincited by 14
  4. 2022Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer Learningcited by 13
  5. 2024Robust Utility-Preserving Text Anonymization Based on Large Language Modelscited by 9
  6. 2022Evidence > Intuition: Transferability Estimation for Encoder Selectioncited by 7
  7. 2024The Empirical Impact of Data Sanitization on Language Modelscited by 6
  8. 2025Automated Privacy Information Annotation in Large Language Model Interactionscited by 6
  9. 2025Efficient Text Encoders for Labor Market Analysiscited by 5
  10. 2023Detecting Fake Job Postings Using Bidirectional LSTMcited by 1
  11. 2025Cross-Domain Transfer and Few-Shot Learning for Personal Identifiable Information Recognitioncited by 0
  12. 2025Enhancing Job Matching: Occupation, Skill and Qualification Linking with the ESCO and EQF taxonomiescited by 0