01927nas a2200181   4500008004100000245013100041210006900172260000800241520131000249100002301559700002501582700001801607700001801625700001501643700001401658700002001672856005301692       2021                        eng d00aBibliometric analysis of named entity recognition for chemoinformatics and biomedical information extraction of ovarian cancer0 aBibliometric analysis of named entity recognition for chemoinfor  cAPR3 aWith 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.
1 aKhedkar, Vijayhsri1 aFernandes, Charlotte1 adesai, Devshi1 aShinde, Mansi1 aDr, Chavan1 aDr, Tidke1 aDr, Karthikeyan  uhttps://digitalcommons.unl.edu/libphilprac/5536/