Université Paris Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique, Orsay, France.
STL, CNRS, Université de Lille, Domaine du Pont-de-bois, Villeneuve-d'Ascq cedex, France.
Yearb Med Inform. 2021 Aug;30(1):257-263. doi: 10.1055/s-0041-1726528. Epub 2021 Sep 3.
To analyze the content of publications within the medical NLP domain in 2020.
Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.
Three best papers have been selected in 2020. We also propose an analysis of the content of the NLP publications in 2020, all topics included.
The two main issues addressed in 2020 are related to the investigation of COVID-related questions and to the further adaptation and use of transformer models. Besides, the trends from the past years continue, such as diversification of languages processed and use of information from social networks.
分析 2020 年医学自然语言处理领域出版物的内容。
自动和手动预筛选要审查的出版物，并选择当年最好的自然语言处理论文。分析重要问题。
2020 年共选出三篇最佳论文。我们还对 2020 年自然语言处理出版物的内容进行了分析，包括所有主题。
2020 年主要讨论了两个问题，一是与调查 COVID 相关问题有关，二是进一步调整和使用变压器模型。此外，过去几年的趋势仍在继续，如处理语言的多样化和使用社交网络信息。
