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。

科学致谢中有监督自动提取和分类命名实体的嵌入模型

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

Analysis of acknowledgments is particularly interesting as acknowledgments
may give information not only about funding, but they are also able to reveal
hidden contributions to authorship and the researcher's collaboration patterns,
context in which research was conducted, and specific aspects of the academic
work. The focus of the present research is the analysis of a large sample of
acknowledgement texts indexed in the Web of Science (WoS) Core Collection.
Record types 'article' and 'review' from four different scientific domains,
namely social sciences, economics, oceanography and computer science, published
from 2014 to 2019 in a scientific journal in English were considered. Six types
of acknowledged entities, i.e., funding agency, grant number, individuals,
university, corporation and miscellaneous, were extracted from the
acknowledgement texts using a Named Entity Recognition (NER) tagger and
subsequently examined. A general analysis of the acknowledgement texts showed
that indexing of funding information in WoS is incomplete. The analysis of the
automatically extracted entities revealed differences and distinct patterns in
the distribution of acknowledged entities of different types between different
scientific domains. A strong association was found between acknowledged entity
and scientific domain and acknowledged entity and entity type. Only negligible
correlation was found between the number of citations and the number of
acknowledged entities. Generally, the number of words in the acknowledgement
texts positively correlates with the number of acknowledged funding
organizations, universities, individuals and miscellaneous entities. At the
same time, acknowledgement texts with the larger number of sentences have more
acknowledged individuals and miscellaneous categories.

通过 WoS 数据集中的科学文献，在四个不同的学科领域（社会科学、经济学、海洋学和计算机科学）上进行了大量的致谢文本分析，在 NER 的帮助下提取了来自不同类型机构的被致谢实体，发现各个实体的分布模式存在着巨大的差异，并与不同的科学领域相关。