@article {47792,
	title = {Bibliometric analysis of emerging technologies in the field of computer science helping in ovarian cancer research},
	journal = {Library Philosophy and Practice},
	year = {2021},
	month = {APR},
	type = {Article},
	abstract = {This study is carried out to provide an analysis of the literature available at the intersection of ovarian cancer and computing. A comprehensive search was conducted using Scopus database for English-language peer-reviewed articles. The study administers chronological, domain clustering and text analysis of the articles under consideration to provide high-level concept map composed of specific words and the connections between them.},
	url = {https://digitalcommons.unl.edu/libphilprac/5590/},
	author = {Kothari, Dr. Sonali and Gupta, Anvita and Agrawal, Muskaan and Jaggi, Kajal and Goswami, Adhiraj and Kotecha, Ketan and Dr, Karthikeyan and Khedkar, Vijayhsri}
}
@article {47793,
	title = {Bibliometric analysis of named entity recognition for chemoinformatics and biomedical information extraction of ovarian cancer},
	journal = {Library Philosophy and Practice },
	year = {2021},
	month = {APR},
	type = {Article},
	abstract = {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.
},
	url = {https://digitalcommons.unl.edu/libphilprac/5536/},
	author = {Khedkar, Vijayhsri and Fernandes, Charlotte and desai, Devshi and Shinde, Mansi and Dr, Chavan, and Dr, Tidke, and Dr, Karthikeyan}
}
