# Evaluation of Embedding Models for Automatic Extraction and Classification of Acknowledged Entities in Scientific Documents

arXiv: https://arxiv.org/abs/2206.10939 · HTML: https://mlsanity.com/paper/2206.10939/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 22 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [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)
- [FLERT: Document-Level Features for Named Entity Recognition](https://mlsanity.com/paper/2011.06993/) (2020)
- [The Journal Coverage of Web of Science and Scopus: a Comparative Analysis](https://mlsanity.com/paper/1511.08096/) (2015)
- [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)

## Built on by (most cited first, 3 of 3)

- [A Comprehensive Analysis of Acknowledgement Texts in Web of Science: a case study on four scientific domains](https://mlsanity.com/paper/2210.09716/) (2022)
- [Evaluation of Word Embeddings for the Social Sciences](https://mlsanity.com/paper/2302.06174/) (2023)
- [Embedding Models for Supervised Automatic Extraction and Classification of Named Entities in Scientific Acknowledgements](https://mlsanity.com/paper/2307.13377/) (2023)

## 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 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.