# De-identification of Privacy-related Entities in Job Postings

arXiv: https://arxiv.org/abs/2105.11223 · HTML: https://mlsanity.com/paper/2105.11223/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 63 references that are in the map.
- First posted on arXiv 24 May 2021.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://mlsanity.com/paper/1802.08232/) (2018)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [De-identification of Patient Notes with Recurrent Neural Networks](https://mlsanity.com/paper/1606.03475/) (2016)
- [BAM! Born-Again Multi-Task Networks for Natural Language Understanding](https://mlsanity.com/paper/1907.04829/) (2019)
- [Portuguese Named Entity Recognition using BERT-CRF](https://mlsanity.com/paper/1909.10649/) (2019)
- [Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP](https://mlsanity.com/paper/2005.14672/) (2020)
- [Code and Named Entity Recognition in StackOverflow](https://mlsanity.com/paper/2005.01634/) (2020)
- [Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss](https://mlsanity.com/paper/1604.05529/) (2016)
- [When is multitask learning effective? Semantic sequence prediction under varying data conditions](https://mlsanity.com/paper/1612.02251/) (2016)
- [Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Records](https://mlsanity.com/paper/1906.05000/) (2019)
- [Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Records](https://mlsanity.com/paper/2001.05714/) (2020)
- [A Deep Learning Architecture for De-identification of Patient Notes: Implementation and Evaluation](https://mlsanity.com/paper/1810.01570/) (2018)
- [De-identification of medical records using conditional random fields and long short-term memory networks](https://mlsanity.com/paper/1709.06901/) (2017)

## Built on by (most cited first, 12 of 12)

- [The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization](https://mlsanity.com/paper/2202.00443/) (2022)
- [SkillSpan: Hard and Soft Skill Extraction from English Job Postings](https://mlsanity.com/paper/2204.12811/) (2022)
- [ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain](https://mlsanity.com/paper/2305.12092/) (2023)
- [Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer Learning](https://mlsanity.com/paper/2205.01381/) (2022)
- [Robust Utility-Preserving Text Anonymization Based on Large Language Models](https://mlsanity.com/paper/2407.11770/) (2024)
- [Evidence > Intuition: Transferability Estimation for Encoder Selection](https://mlsanity.com/paper/2210.11255/) (2022)
- [The Empirical Impact of Data Sanitization on Language Models](https://mlsanity.com/paper/2411.05978/) (2024)
- [Automated Privacy Information Annotation in Large Language Model Interactions](https://mlsanity.com/paper/2505.20910/) (2025)
- [Efficient Text Encoders for Labor Market Analysis](https://mlsanity.com/paper/2505.24640/) (2025)
- [Detecting Fake Job Postings Using Bidirectional LSTM](https://mlsanity.com/paper/2304.02019/) (2023)
- [Cross-Domain Transfer and Few-Shot Learning for Personal Identifiable Information Recognition](https://mlsanity.com/paper/2507.11862/) (2025)
- [Enhancing Job Matching: Occupation, Skill and Qualification Linking with the ESCO and EQF taxonomies](https://mlsanity.com/paper/2512.03195/) (2025)

## 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.