Smirnova, N., Mayr, P. Embedding models for supervised automatic extraction and classification of named entities in scientific acknowledgements. Scientometrics 129, 7261–7285 (2024).
https://doi.org/10.1007/s11192-023-04806-2
Acknowledgments in scientific papers may give an insight into aspects of the scientific community, such as reward systems, collaboration patterns, and hidden research trends. The aim of the paper is to evaluate the performance of different embedding models for the task of automatic extraction and classification of acknowledged entities from the acknowledgment text in scientific papers. The authors develop a model which can be applied for the comprehensive analysis of acknowledgment texts and may potentially make a great contribution to the field of automated acknowledgment analysis.