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Acknowledgments in scientific papers may give an insight into aspects of thescientific community, such as reward systems, collaboration patterns, andhidden research trends. The aim of the paper is to evaluate the performance ofdifferent embedding models for the task of automatic extraction andclassification of acknowledged entities from the acknowledgment text inscientific papers. We trained and implemented a named entity recognition (NER)task using the Flair NLP framework. The training was conducted using threedefault Flair NER models with four differently-sized corpora and differentversions of the Flair NLP framework. The Flair Embeddings model trained on themedium 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 sizemassively increased the accuracy of all training algorithms, but furtherexpansion of the training corpus did not bring further improvement. Moreover,the performance of the model slightly deteriorated. Our model is able torecognize six entity types: funding agency, grant number, individuals,university, corporation, and miscellaneous. The model works more precisely forsome entity types than for others; thus, individuals and grant numbers showed avery good F1-Score over 0.9. Most of the previous works on acknowledgmentanalysis were limited by the manual evaluation of data and therefore by theamount of processed data. This model can be applied for the comprehensiveanalysis of acknowledgment texts and may potentially make a great contributionto the field of automated acknowledgment analysis.

Embedding Models for Supervised Automatic Extraction and Classification of Named Entities in Scientific Acknowledgements