De-identification of Privacy-related Entities in Job Postings

• URL: http://arxiv.org/abs/2105.11223v1
• Date: Mon, 24 May 2021 12:01:22 GMT
• Title: De-identification of Privacy-related Entities in Job Postings
• Authors: Kristian N{\o}rgaard Jensen, Mike Zhang, Barbara Plank
• Abstract summary: De-identification is the task of detecting privacy-related entities in text, such as person names, emails and contact data. We present JobStack, a new corpus for de-identification of personal data in job vacancies on Stackoverflow.
• Score: 10.751883216434717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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