A Year of Papers Using Biomedical Texts:

Abstract

Objectives: Analyze papers published in 2019 within the medical natural language processing (NLP) domain in order to select the best works of the field. Methods: We performed an automatic and manual pre-selection of papers to be reviewed and finally selected the best NLP papers of the year. We also propose an analysis of the content of NLP publications in 2019. Results: Three best papers have been selected this year including the generation of synthetic record texts in Chinese, a method to identify contradictions in the literature, and the BioBERT word representation. Conclusions: The year 2019 was very rich and various NLP issues and topics were addressed by research teams. This shows the will and capacity of researchers to move towards robust and reproducible results. Researchers also prove to be creative in addressing original issues with relevant approaches.

Informatics for One Health

“…The authors presented numerous annotated datasets and various NLP tasks, with a strong emphasis on the use of pre-trained language models for these tasks. The synopsis of the NLP section, Cyril Grouin and Natalia Grabar, provides a comprehensive description of works published in 2022 [31]. After listing the languages studied in these recent works, they also underline the enthusiasm for large language models and note that the fact that most of them are freely available facilitates the democratization of NLP.…”