@inproceedings{aghajani-etal-2021-parstwiner,
    title = "{P}ars{T}wi{NER}: A Corpus for Named Entity Recognition at Informal {P}ersian",
    author = "Aghajani, MohammadMahdi  and
      Badri, AliAkbar  and
      Beigy, Hamid",
    editor = "Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim  and
      Rahimi, Afshin",
    booktitle = "Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)",
    month = nov,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.wnut-1.16/",
    doi = "10.18653/v1/2021.wnut-1.16",
    pages = "131--136",
    abstract = "As a result of unstructured sentences and some misspellings and errors, finding named entities in a noisy environment such as social media takes much more effort. ParsTwiNER contains about 250k tokens, based on standard instructions like MUC-6 or CoNLL 2003, gathered from Persian Twitter. Using Cohen`s Kappa coefficient, the consistency of annotators is 0.95, a high score. In this study, we demonstrate that some state-of-the-art models degrade on these corpora, and trained a new model using parallel transfer learning based on the BERT architecture. Experimental results show that the model works well in informal Persian as well as in formal Persian."
}