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

arXiv: https://arxiv.org/abs/2307.13377 · HTML: https://mlsanity.com/paper/2307.13377/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling](https://mlsanity.com/paper/1312.3005/) (2013)
- [Named Entity Recognition as Dependency Parsing](https://mlsanity.com/paper/2005.07150/) (2020)
- [FLERT: Document-Level Features for Named Entity Recognition](https://mlsanity.com/paper/2011.06993/) (2020)
- [SsciBERT: A Pre-trained Language Model for Social Science Texts](https://mlsanity.com/paper/2206.04510/) (2022)
- [The Journal Coverage of Web of Science, Scopus and Dimensions: A Comparative Analysis](https://mlsanity.com/paper/2011.00223/) (2020)
- [Making visible the invisible through the analysis of acknowledgements in the humanities](https://mlsanity.com/paper/1706.06334/) (2017)
- [Evaluation of Embedding Models for Automatic Extraction and Classification of Acknowledged Entities in Scientific Documents](https://mlsanity.com/paper/2206.10939/) (2022)
- [A Comprehensive Analysis of Acknowledgement Texts in Web of Science: a case study on four scientific domains](https://mlsanity.com/paper/2210.09716/) (2022)

## Abstract (arXiv)

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.