Jensen, Kristian Nørgaard

Plank, Barbara

Zhang, Mike

English

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

The IT University of Copenhagen's Repository

De-identification of Privacy-related Entities in Job Postings

https://pure.itu.dk/portal/files/85880206/JobStack_NoDaLiDa_2021.pdf

De-identification of Privacy-related Entities in Job Postings

Abstract

Similar works

Full text

Available Versions

The IT University of Copenhagen's Repository