Text Zoning and Classification for Job Advertisements in German, French and English

摘要

We present experiments to structure job ads into text zones and classify them into professions, industries and management functions, thereby facilitating social science analyses on labor marked demand. Our main contribution arc empirical findings on the benefits of contextualized embeddings and the potential of multi-task models for this purpose. With contextualized in-domain embeddings in BiLSTM-CRF models, we reach an accuracy of 91% for token-level text zoning and outperform previous approaches. A multi-tasking BERT model performs well for our classification tasks. We further compare transfer approaches for our multilingual data.