Papers by Marcel Milich

    1 papers
    ZELDA: A Comprehensive Benchmark for Supervised Entity Disambiguation (2023.eacl-main)

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    Challenge: Entity disambiguation (ED) is the task of disambiguating named entity mentions in text to unique entries in a knowledge base.
    Approach: They propose a benchmark for entity disambiguation that includes a unified training data set, entity vocabulary, candidate lists and challenging evaluation splits covering 8 different domains.
    Outcome: The proposed benchmark is based on a unified training data set, entity vocabulary, candidate lists and evaluation splits covering 8 different domains.

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