2020
EMNLP
EMNLP 2020
Text Zoning and Classification for Job Advertisements in German, French and English
Abstract
AbstractWe present experiments to structure job ads into text zones and classify them into pro- fessions, industries and management functions, thereby facilitating social science analyses on labor marked demand. Our main contribution are 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.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Interdisciplinary and Machine Learning
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Keyword Pioneer
— text zoning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Learning Paradigms > Transfer Learning
Machine Learning > Core Methods > Classification
Interdisciplinary > Linguistics > Computational Linguistics
Machine Learning > Learning Types > Multi-Task Learning
Machine Learning > Learning Types > Transfer Learning
Machine Learning > Learning Paradigms > Multi-Task Learning
Deep Learning > Learning Types > Multi-Task Learning
Artificial Intelligence > Core AI > Natural Language Processing
Machine Learning > Application Areas > Text Classification