%0 Conference Proceedings
%T ParsTwiNER: A Corpus for Named Entity Recognition at Informal Persian
%A Aghajani, MohammadMahdi
%A Badri, AliAkbar
%A Beigy, Hamid
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F aghajani-etal-2021-parstwiner
%X 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.
%R 10.18653/v1/2021.wnut-1.16
%U https://aclanthology.org/2021.wnut-1.16
%U https://doi.org/10.18653/v1/2021.wnut-1.16
%P 131-136