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- 01HDNKFAFWHB08J12A55DCN7GD classification C1.
- 01HDNKFAFWHB08J12A55DCN7GD date "2023".
- 01HDNKFAFWHB08J12A55DCN7GD language "eng".
- 01HDNKFAFWHB08J12A55DCN7GD type conference.
- 01HDNKFAFWHB08J12A55DCN7GD hasPart 01HDNKK5FTNARV91HANPRB07XG.pdf.
- 01HDNKFAFWHB08J12A55DCN7GD hasPart urn:uuid:1a840348-64a4-4896-a845-c0778581387c.
- 01HDNKFAFWHB08J12A55DCN7GD subject "Languages and Literatures".
- 01HDNKFAFWHB08J12A55DCN7GD subject "Social Sciences".
- 01HDNKFAFWHB08J12A55DCN7GD subject "Technology and Engineering".
- 01HDNKFAFWHB08J12A55DCN7GD presentedAt urn:uuid:d97df1d2-4d1a-4a94-b5c1-85bdd83b6c67.
- 01HDNKFAFWHB08J12A55DCN7GD abstract "Online job ads serve as a valuable source of information for skill requirements, playing a crucial role in labor market analysis and erecruitment processes. Since such ads are typically formatted in free text, natural language processing (NLP) technologies are required to automatically process them. We specifically focus on the task of detecting skills (mentioned literally, or implicitly described) and linking them to a large skill ontology, making it a challenging case of extreme multi-label classification (XMLC). Given that there is no sizable labeled (training) dataset are available for this specific XMLC task, we propose techniques to leverage general Large Language Models (LLMs). We describe a cost-effective approach to generate an accurate, fully synthetic labeled dataset for skill extraction, and present a contrastive learning strategy that proves effective in the task. Our results across three skill extraction benchmarks show a consistent increase of between 15 to 25 percentage points in RPrecision@5 compared to previously published results that relied solely on distant supervision through literal matches".
- 01HDNKFAFWHB08J12A55DCN7GD author 38D233E4-72EB-11E6-B59D-C1A0B5D1D7B1.
- 01HDNKFAFWHB08J12A55DCN7GD author 73C7BFA4-51BE-11E5-8004-D5C4B4D1D7B1.
- 01HDNKFAFWHB08J12A55DCN7GD author D1FCCFFC-28B5-11E4-8B6F-07F0B4D1D7B1.
- 01HDNKFAFWHB08J12A55DCN7GD author F5DC76B8-F0ED-11E1-A9DE-61C894A0A6B4.
- 01HDNKFAFWHB08J12A55DCN7GD author F79F4B9C-F0ED-11E1-A9DE-61C894A0A6B4.
- 01HDNKFAFWHB08J12A55DCN7GD author F7F44048-F0ED-11E1-A9DE-61C894A0A6B4.
- 01HDNKFAFWHB08J12A55DCN7GD dateCreated "2023-10-26T09:01:32Z".
- 01HDNKFAFWHB08J12A55DCN7GD dateModified "2024-10-29T18:33:25Z".
- 01HDNKFAFWHB08J12A55DCN7GD name "Extreme multi-label skill extraction training using large language models".
- 01HDNKFAFWHB08J12A55DCN7GD pagination urn:uuid:7fb92c9a-4e1e-4c5f-aa00-a5a5332f4d0c.
- 01HDNKFAFWHB08J12A55DCN7GD sameAs LU-01HDNKFAFWHB08J12A55DCN7GD.
- 01HDNKFAFWHB08J12A55DCN7GD sourceOrganization urn:uuid:c1b82b77-3342-458f-866c-c37a49fd050c.
- 01HDNKFAFWHB08J12A55DCN7GD sourceOrganization urn:uuid:e3174725-ed88-437a-acec-fd097413d5bf.
- 01HDNKFAFWHB08J12A55DCN7GD type C1.