论文标题
Beheshti-ner：波斯语命名实体识别使用Bert
Beheshti-NER: Persian Named Entity Recognition Using BERT
论文作者
论文摘要
命名实体识别是一项自然语言处理任务，旨在识别和提取与命名实体相关的文本跨度并将其分类为语义类别。 Google Bert是一种深厚的双向语言模型，可以在大型语料库中进行培训，可以通过微调来解决许多NLP任务，例如问答，命名intity识别，语音标记等等。在本文中，我们使用预先训练的深度双向网络，伯特（Bert），伯特（Bert），以在波斯语中为命名的独立性识别模型。 我们还将模型的结果与波斯NER的先前最先进的结果进行了比较。我们的评估度量是Conll 2003分两个单词和短语的得分。该模型在NSURL-2019 Task 7竞赛中获得了第二名，该竞赛与NER有关波斯语。我们在这项竞争中的结果分别为83.5和88.4 F1 CONLL评分在短语和单词级别评估中。
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.
