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Sökning: WFRF:(Dietrich M.) > Luleå tekniska universitet

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1.
  • Farley, K.A., et al. (författare)
  • In situ radiometric and exposure age dating of the martian surface
  • 2014
  • Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 343:6169
  • Tidskriftsartikel (refereegranskat)abstract
    • We determined radiogenic and cosmogenic noble gases in a mudstone on the floor of Gale Crater. A K-Ar age of 4.21 ± 0.35 billion years represents a mixture of detrital and authigenic components and confirms the expected antiquity of rocks comprising the crater rim. Cosmic-ray-produced 3He, 21Ne, and 36Ar yield concordant surface exposure ages of 78 ± 30 million years. Surface exposure occurred mainly in the present geomorphic setting rather than during primary erosion and transport. Our observations are consistent with mudstone deposition shortly after the Gale impact or possibly in a later event of rapid erosion and deposition. The mudstone remained buried until recent exposure by wind-driven scarp retreat. Sedimentary rocks exposed by this mechanism may thus offer the best potential for organic biomarker preservation against destruction by cosmic radiation.
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2.
  • Adelani, David Ifeoluwa, et al. (författare)
  • MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition
  • 2022
  • Ingår i: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. - : Association for Computational Linguistics (ACL). ; , s. 4488-4508
  • Konferensbidrag (refereegranskat)abstract
    • African languages are spoken by over a billion people, but are underrepresented in NLP research and development. The challenges impeding progress include the limited availability of annotated datasets, as well as a lack of understanding of the settings where current methods are effective. In this paper, we make progress towards solutions for these challenges, focusing on the task of named entity recognition (NER). We create the largest human-annotated NER dataset for 20 African languages, and we study the behavior of state-of-the-art cross-lingual transfer methods in an Africa-centric setting, demonstrating that the choice of source language significantly affects performance. We show that choosing the best transfer language improves zero-shot F1 scores by an average of 14 points across 20 languages compared to using English. Our results highlight the need for benchmark datasets and models that cover typologically-diverse African languages.
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