论文标题
命名实体识别实验协议的域特定BERT表示
Domain specific BERT representation for Named Entity Recognition of lab protocol
论文作者
论文摘要
受过培训的监督模型可以预测代表性的属性，这已经在各种任务上达到了高精度。例如，Bert家族似乎在从NER标记到其他语言任务范围的下游任务上表现出色。但是，医学领域中使用的词汇包含仅在医疗行业中使用的许多不同令牌，例如不同疾病，设备，生物，药物等的名称，使传统的BERT模型很难创建上下文化的嵌入。在本文中，我们将说明基于Bio-Bert的命名实体标记的系统。实验结果表明，我们的模型比基线给出了实质性的改进，并以F1分数获得了第四名亚军，而在召回率方面，第一名的冠军仅落后2.21 F1得分。
Supervised models trained to predict properties from representations have been achieving high accuracy on a variety of tasks. For instance, the BERT family seems to work exceptionally well on the downstream task from NER tagging to the range of other linguistic tasks. But the vocabulary used in the medical field contains a lot of different tokens used only in the medical industry such as the name of different diseases, devices, organisms, medicines, etc. that makes it difficult for traditional BERT model to create contextualized embedding. In this paper, we are going to illustrate the System for Named Entity Tagging based on Bio-Bert. Experimental results show that our model gives substantial improvements over the baseline and stood the fourth runner up in terms of F1 score, and first runner up in terms of Recall with just 2.21 F1 score behind the best one.
