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 four differently-sized corpora and different
versions of the Flair NLP framework. The Flair Embeddings model trained on the
medium corpus with the latest FLAIR version showed the best accuracy of 0.79.
Expanding the size of a training corpus from very small to medium size
massively increased the accuracy of all training algorithms, but further
expansion of the training corpus did not bring further improvement. Moreover,
the performance of the model slightly deteriorated. Our model is able to
recognize six entity types: funding agency, grant number, individuals,
university, corporation, and miscellaneous. The model works more precisely for
some entity types than for others; thus, individuals and grant numbers showed a
very good F1-Score over 0.9. Most of the previous works on acknowledgment
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 acknowledgment texts and may potentially make a great contribution
to the field of automated acknowledgment analysis.

该研究评估使用不同嵌入模型自动提取和分类科学论文中致谢文本中的承认实体的性能，并通过 Flair NLP 框架进行命名实体识别任务。最佳准确率为 0.79，实体类型包括资助机构、授予号、个人、大学、公司和其他。此模型可用于对致谢文本进行综合分析，对自动化致谢分析领域可能具有重大贡献。