  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. We trained and implemented a named entity recognition (NER)
task using the Flair NLP-framework. The training was conducted using three
default Flair NER models with two differently-sized corpora. The Flair
Embeddings model trained on the larger training corpus showed the best accuracy
of 0.77. Our model is able to recognize six entity types: funding agency, grant
number, individuals, university, corporation and miscellaneous. The model works
more precise for some entity types than the others, thus, individuals and grant
numbers showed very good F1-Score over 0.9. Most of the previous works on
acknowledgement analysis were limited by the manual evaluation of data and
therefore by the amount of processed data. This model can be applied for the
comprehensive analysis of the acknowledgement texts and may potentially make a
great contribution to the field of automated acknowledgement analysis.
