Papers by Ann-Sophie Gnehm
Improving Occupational ISCO Classification of Multilingual Swiss Job Postings with LLM-Refined Training Data (2025.findings-acl)
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| Challenge: | 80% of job postings are German, 11% French, 8% English, and under 1% Italian. |
| Approach: | They propose a method that refines silver-standard ISCO labels by consolidating them with predictions from pre-fine-tuned models to resolve discrepancies. |
| Outcome: | The proposed method raises Top-1 accuracy on silver data to 58.3% and reaches 80% precision on held-out data. |
Evaluation of Transfer Learning and Domain Adaptation for Analyzing German-Speaking Job Advertisements (2022.lrec-1)
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| Challenge: | a paper presents text mining approaches on German-speaking job advertisements . transfer learning and domain adaptation are used to build text mining applications . |
| Approach: | They propose text mining approaches on German-speaking job advertisements . they use transfer learning and domain adaptation to build language models adapted to job ads . |
| Outcome: | The proposed approaches outperform general-domain language models pre-trained on ten times more data. |
Mapping Work Task Descriptions from German Job Ads on the O*NET Work Activities Ontology (2024.lrec-main)
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| Challenge: | a new method for mapping job tasks to labor market ontologies is proposed . a top configuration of the method achieved a notable performance improvement . |
| Approach: | They use ontological data with Multiple Negatives Ranking loss to extract job tasks from job postings . they integrate labeled job advertisement data into training to improve their mapping . |
| Outcome: | The proposed method improves on the German job ads and their ontology . it can be used to map job tasks to established labor market ontologies or taxonomies . |