{
 "id": "cardiffnlp/tweet_ter",
 "owner": "cardiffnlp",
 "name": "tweet_ter",
 "downloads": 28,
 "likes": 1,
 "last_modified": "2024-05-21T02:27:26.000Z",
 "stated_licence": "unknown",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [
  {
   "id": "other",
   "licence": null,
   "found": false,
   "evidence": "https://huggingface.co/datasets/other"
  }
 ],
 "gated": false,
 "tasks": [
  "other"
 ],
 "citation": "@inproceedings{rezaee-etal-2024-tweetter-benchmark,\n    title = \"{T}weet{TER}: A Benchmark for Target Entity Retrieval on {T}witter without Knowledge Bases\",\n    author = \"Rezaee, Kiamehr  and\n      Camacho-Collados, Jose  and\n      Pilehvar, Mohammad Taher\",\n    editor = \"Calzolari, Nicoletta  and\n      Kan, Min-Yen  and\n      Hoste, Veronique  and\n      Lenci, Alessandro  and\n      Sakti, Sakriani  and\n      Xue, Nianwen\",\n    booktitle = \"Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)\",\n    month = may,\n    year = \"2024\",\n    address = \"Torino, Italy\",\n    publisher = \"ELRA and ICCL\",\n    url = \"https://aclanthology.org/2024.lrec-main.1468\",\n    pages = \"16890--16896\",\n    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",
 "signal": "unclear",
 "reason": "The checks could not identify a recognised licence ID in the dataset's metadata.",
 "revision": "e3a75da31d34386435d2583a086e9b2876981acc",
 "stated_evidence": "https://huggingface.co/datasets/cardiffnlp/tweet_ter/blob/e3a75da31d34386435d2583a086e9b2876981acc/README.md",
 "file_evidence": "https://huggingface.co/datasets/cardiffnlp/tweet_ter/tree/e3a75da31d34386435d2583a086e9b2876981acc",
 "card_nc": false
}