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Found 3 papers, 1 papers with code
Embedding Models for Supervised Automatic Extraction and Classification of Named Entities in Scientific Acknowledgements
The Flair Embeddings model trained on the medium corpus with the latest FLAIR version showed the best accuracy of 0. 79.
A Comprehensive Analysis of Acknowledgement Texts in Web of Science: a case study on four scientific domains
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.
Evaluation of Embedding Models for Automatic Extraction and Classification of Acknowledged Entities in Scientific Documents
We trained and implemented a named entity recognition (NER) task using the Flair NLP-framework.
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