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ParsTwiNER: A Corpus for Named Entity Recognition at Informal Persian

2021-11-01 · WNUT (ACL) 2021 11 · MohammadMahdi Aghajani, AliAkbar Badri, Hamid Beigy

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.

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Code (1)

overfit-ir/parstwiner 공식 구현 pytorch

Tasks

named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Transfer Learning

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