Beheshti-NER: Persian Named Entity Recognition Using BERT
This work addresses NER for Persian, an under-resourced language, but is incremental as it applies an existing method to new data.
The authors tackled Persian named entity recognition by fine-tuning BERT, achieving competitive results with F1 scores of 83.5 and 88.4 at phrase and word levels, respectively, in a competition.
Named entity recognition is a natural language processing task to recognize and extract spans of text associated with named entities and classify them in semantic Categories. Google BERT is a deep bidirectional language model, pre-trained on large corpora that can be fine-tuned to solve many NLP tasks such as question answering, named entity recognition, part of speech tagging and etc. In this paper, we use the pre-trained deep bidirectional network, BERT, to make a model for named entity recognition in Persian. We also compare the results of our model with the previous state of the art results achieved on Persian NER. Our evaluation metric is CONLL 2003 score in two levels of word and phrase. This model achieved second place in NSURL-2019 task 7 competition which associated with NER for the Persian language. our results in this competition are 83.5 and 88.4 f1 CONLL score respectively in phrase and word level evaluation.