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With the massive amount of data that has been generated in the form of unstructured text documents, Biomedical Named Entity Recognition (BioNER) is becoming increasingly important in the field of biomedical research. Since currently there does not exist any automatic archiving of the obtained results, a lot of this information remains hidden in the textual details and is not easily accessible for further analysis. Hence, text mining methods and natural language processing techniques are used for the extraction of information from such publications.Named entity recognition, is a subtask that comes under information extraction that focuses on finding and categorizing specific entities in text.
In this paper, bibliometric analysis of named entity recognition of ovarian cancer is carried out using information about publications from Scopus. The most productive journals, countries and authors are determined. The most frequently cited article and its citation history has been described. Also bibliometric maps based on citation network among countries are constructed.
This study can assist people in the medical field to get a comprehensive understanding of the study of BioNER. It can also be utilized for reference works, for the research and application of the BioNER visualization methods.