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.

本文使用预训练的深度双向网络 Google BERT，建立了一个用于波斯语命名实体识别的模型，并在 CONLL 2003 评测任务中获得了较高的成绩。

使用 BERT 的波斯语命名实体识别模型 Beheshti-NER

Beheshti-NER: Persian Named Entity Recognition Using BERT

The emergence of the digital book is a major step forward in providing access
to reading, and therefore often to the common culture and the labour market. By
allowing the enrichment of texts with cognitive crutches, EPub 3 compatible
accessibility formats such as FROG have proven their effectiveness in
alleviating but also reducing dyslexic disorders. In this paper, we show how
Artificial Intelligence and particularly Transfer Learning with Google BERT can
automate the division into units of meaning, and thus facilitate the creation
of enriched digital books at a moderate cost.

本文介绍数字图书为阅读提供便利，特别是对于对于阅读有困难的人，EPub 3 的辅助功能格式（如 FROG）以及采用人工智能中的 Transfer Learning 技术和 Google BERT 等技术，能够自动划分意义单元，从而降低数字图书的制作成本和提升辅助功能。