Abstract
Curriculum vitae or resume screening is a time-consuming procedure. Natural language processing and machine learning have the capability to understand and parse the unstructured written language, and extract the desired information. The idea is to train the machine to analyze the written documents like a human being. This paper presents a systematic review on resume screening and enlightens the comparison of recognized works. Several techniques and approaches of machine learning for evaluating and analyzing the unstructured data have been discussed. Existing resume parsers use semantic search to understand the context of the language in order to find the reliable and comprehensive results. A review on the use of semantic search for context-based searching has been explained. In addition, this paper also shows the research challenges and future scope of resume parsing in terms of writing style, word choice and syntax of unstructured written language.
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Sinha, A.K., Amir Khusru Akhtar, M., Kumar, A. (2021). Resume Screening Using Natural Language Processing and Machine Learning: A Systematic Review. In: Swain, D., Pattnaik, P.K., Athawale, T. (eds) Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1311. Springer, Singapore. https://doi.org/10.1007/978-981-33-4859-2_21
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