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Embedding models for supervised automatic extraction and classification of named entities in scientific acknowledgements

[journal article]

Smirnova, Nina
Mayr, Philipp

Abstract

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 cl... view more

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.... view less


Danksagungen in wissenschaftlichen Arbeiten können einen Einblick in Aspekte der wissenschaftlichen Gemeinschaft geben, wie z.B. Belohnungssysteme, Kooperationsmuster und versteckte Forschungstrends. Das Ziel dieser Arbeit ist es, die Leistung verschiedener Einbettungsmodelle für die Aufgabe der aut... view more

Danksagungen in wissenschaftlichen Arbeiten können einen Einblick in Aspekte der wissenschaftlichen Gemeinschaft geben, wie z.B. Belohnungssysteme, Kooperationsmuster und versteckte Forschungstrends. Das Ziel dieser Arbeit ist es, die Leistung verschiedener Einbettungsmodelle für die Aufgabe der automatischen Extraktion und Klassifizierung von anerkannten Entitäten aus dem Danksagungstext in wissenschaftlichen Arbeiten zu bewerten.... view less

Classification
Scientometrics, Bibliometrics, Informetrics

Free Keywords
Natural language processing; Named entity recognition; Web of science; Acknowledgement; Text mining; Flair NLP-framework

Document language
English

Publication Year
2024

Page/Pages
p. 7261-7285

Journal
Scientometrics, 129 (2024) 11

Issue topic
Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE 2022)

DOI
https://doi.org/10.1007/s11192-023-04806-2

ISSN
1588-2861

Status
Published Version; peer reviewed

Licence
Creative Commons - Attribution 4.0


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© 2007 - 2025 Social Science Open Access Repository (SSOAR).
Based on DSpace, Copyright (c) 2002-2022, DuraSpace. All rights reserved.
 

 

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