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        <title>Text Zoning and Classification for Job Advertisements in German, French and English</title>
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        <namePart type="given">Ann-Sophie</namePart>
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            <namePart type="given">David</namePart>
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    <abstract>We 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.</abstract>
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%0 Conference Proceedings
%T Text Zoning and Classification for Job Advertisements in German, French and English
%A Gnehm, Ann-Sophie
%A Clematide, Simon
%Y Bamman, David
%Y Hovy, Dirk
%Y Jurgens, David
%Y O’Connor, Brendan
%Y Volkova, Svitlana
%S Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F gnehm-clematide-2020-text
%X We 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.
%R 10.18653/v1/2020.nlpcss-1.10
%U https://aclanthology.org/2020.nlpcss-1.10
%U https://doi.org/10.18653/v1/2020.nlpcss-1.10
%P 83-93

Markdown (Informal)

[Text Zoning and Classification for Job Advertisements in German, French and English](https://aclanthology.org/2020.nlpcss-1.10) (Gnehm & Clematide, NLP+CSS 2020)

• Text Zoning and Classification for Job Advertisements in German, French and English (Gnehm & Clematide, NLP+CSS 2020)

ACL

• Ann-Sophie Gnehm and Simon Clematide. 2020. Text Zoning and Classification for Job Advertisements in German, French and English. In Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pages 83–93, Online. Association for Computational Linguistics.