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Data Science for Innovation

@inno_data

Research unit Data Science for Innovation @Fraunhofer IMW. We make knowledge accessible, understandable and useful for science, industry, politics, and society.

 Leipzig, Germany
s.fhg.de/innodata
Joined October 2021
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📢We are excited to announce our paper " Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings" has been accepted to #EACL2024 at the @nlp4hr workshop. In cooperation with @NLPnorth and @MaiNLPlab.
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🧠 From LSTM to BERT, our paper tracks the evolution of neural methods in skill analysis, emphasizing the shift towards more sophisticated models.
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With @ElenaSngr, @mjjzha, @robvanderg and @barbara_plank Paper: arxiv.org/abs/2402.05617
Deep Learning-based Computational Job Market Analysis: A Survey on...

Deep Learning-based Computational Job Market Analysis: A Survey on...

Recent years have brought significant advances to Natural Language Processing (NLP), which enabled fast progress in the field of computational job market analysis. Core tasks in this application...

arxiv.org
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Great day at the Global Tech Mining Conference, at the TechMining for Collaboration track! Thank you, for the presenters: @jape_b @ArhoSuominen and Bruna Fonseca! And for the session chair Li Tang.
Image from Data Science for Innovation (@inno_data)
Image from Data Science for Innovation (@inno_data)
Image from Data Science for Innovation (@inno_data)
Image from Data Science for Innovation (@inno_data)
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Catch up tomorrow @jape_b presenting our work on Wikification of companies' about pages for partner selection at the #GlobalTechMiningOnline 2:15 PM CET.
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Free registration at gtmconference.org/
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Hello, World!
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