Abstract
Personal webpages of researchers or faculty members make up a percentage of the academic web. These webpages contain semi-structured or plain text information, and research has shown the importance of combining information extracted from multiple academic websites to create a unified database that can help in expert finding, and thus improve information retrieval for end users. This research identifies the kind of named entities that could be present in academic biographies by manually examining the biographies extracted from ORCID public profiles, and describes a method that uses natural language processing techniques and supervised machine learning to automatically extract these named entities from the plain text biographies. Up to 86% accuracy was achieved with support vector machines, demonstrating that the method used in this research can be suitable for creating a reusable trained model that extracts useful academic information from researchers’ personal profiles in webpages or other data sources.
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Acknowledgements
This research was supported by the Tertiary Education Trust Fund (TETFUND). The author would like to thank the anonymous reviewers for their constructive comments and suggestions to improve the quality paper.
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Kenekayoro, P. Identifying named entities in academic biographies with supervised learning. Scientometrics 116, 751–765 (2018). https://doi.org/10.1007/s11192-018-2797-4
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DOI: https://doi.org/10.1007/s11192-018-2797-4
Keywords
- Named entity recognition
- Supervised learning
- Natural language processing
- Support vector machines
- Random forests
- Conditional random fields