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<i>Verbundprojekt: Smart Data Web : zusammenfassender Abschlussbericht : Vorhaben: Verbundprojekt SDW Smart Data Web</i>

, , , , , , , , , , , , , , , , , , и . Kaiserslautern, (5 submitted)

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Leonhard Hennig

Dorothee Hennig

Störungen des Arbeitsgedächtnisses als exekutive Funktion bei Patienten mit einer obstruktiven Schlafapnoe am Beispiel von N-back-Aufgaben. Uni Gießen, (2008)

Christian Hennig

Hydrid-Gasphasenepitaxie von versetzungsarmen und freistehenden GaN-Schichten. TU Berlin, (2009)

Roland Hennig

Cornelia Hennig

 

Другие публикации лиц с тем же именем

Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction., , и . ACL (1), стр. 1388-1398. Association for Computational Linguistics, (2019)Probing Linguistic Features of Sentence-Level Representations in Relation Extraction., , и . ACL, стр. 1534-1545. Association for Computational Linguistics, (2020)Improving Relation Extraction by Pre-trained Language Representations., , и . AKBC, (2019)Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task CNNs., , и . WASSA@EMNLP, стр. 316-321. Association for Computational Linguistics, (2018)MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset., , и . ACL (1), стр. 3785-3801. Association for Computational Linguistics, (2023)A Crowdsourcing Approach to Evaluate the Quality of Query-based Extractive Text Summaries., , , , и . QoMEX, стр. 1-3. IEEE, (2019)A Corpus Study and Annotation Schema for Named Entity Recognition and Relation Extraction of Business Products., , , и . CoRR, (2020)Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction., , и . CoRR, (2020)<i>Verbundprojekt: Smart Data Web : zusammenfassender Abschlussbericht : Vorhaben: Verbundprojekt SDW Smart Data Web</i>, , , , , , , , , и 9 other автор(ы). Kaiserslautern, (05 submitted)A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition., , , , и . RepL4NLP@ACL, стр. 46-59. Association for Computational Linguistics, (2022)