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Vita

Nina Smirnova is a research associate at the department Knowledge Technologies for the Social Sciences (KTS) in team Information & Data Retrieval. She graduated from the University of Bonn with a Master's degree in Applied Linguistics. Her spheres of interest lie in the fields of computational linguistics and data science. She is strongly interested and have an expertise in Natural Language Processing, more specifically in Information Extraction and Classification, and Machine Learning.
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Publications

Publication

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Journal article

Smirnova, Nina, and Philipp Mayr. 2023. "A comprehensive analysis of acknowledgement texts in Web of Science: a case study on four scientific domains." Scientometrics 128 (1): 709–734. doi: https://doi.org/10.1007/s11192-022-04554-9.

Smirnova, Nina, and Philipp Mayr. 2023. "Embedding models for supervised automatic extraction and classification of named entities in scientific acknowledgements." Scientometrics 2023. doi: https://doi.org/10.1007/s11192-023-04806-2.

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Contribution to edited volume

Smirnova, Nina, and Philipp Mayr. 2022. "Evaluation of Embedding Models for Automatic Extraction and Classification of Acknowledged Entities in Scientific Documents." In Proceedings of the 3rd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE 2022) , edited by Chengzhi Zhang, Philipp Mayr, Wei Lu, and Yi Zhang, 48-55. Aachen: CEUR. http://ceur-ws.org/Vol-3210/paper5.pdf.

Lecture

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Conference contribution

Smirnova, Nina. 2022. "Evaluation of Embedding Models for Automatic Extraction and Classification of Acknowledged Entities in Scientific Documents." 3rd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2022) at the ACM/IEEE Joint Conference on Digital Libraries 2022 (JCDL2022), Cologne, Germany and Online, Art’otel in Cologne, 2022-06-23. https://eeke-workshop.github.io/2022/submissions/EEKE2022_paper_4.pdf.