cardiffnlp/tweet_ter

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Citation text from the card

@inproceedings{rezaee-etal-2024-tweetter-benchmark,
    title = "{T}weet{TER}: A Benchmark for Target Entity Retrieval on {T}witter without Knowledge Bases",
    author = "Rezaee, Kiamehr  and
      Camacho-Collados, Jose  and
      Pilehvar, Mohammad Taher",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italy",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1468",
    pages = "16890--16896",
    abstract = "Entity linking is a well-established task in NLP consisting of associating entity mentions with entries in a knowledge base. Current models have demonstrated competitive performance in standard text settings. However, when it comes to noisy domains such as social media, certain challenges still persist. Typically, to evaluate entity linking on existing benchmarks, a comprehensive knowledge base is necessary and models are expected to possess an understanding of all the entities contained within the knowledge base. However, in practical scenarios where the objective is to retrieve sentences specifically related to a particular entity, strict adherence to a complete understanding of all entities in the kn

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