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Domain and context
The ever-increasing number of applications for vacancies poses a challenge for employers to manually find suitable candidates. We present a solution intended to reduce the burden of the recruiter/employer involved in finding suitable candidates.
The CV parser automatically separates candidate information based on various fields and parameters such as name, phone/mobile numbers. etc. The sheer volume of resumes is not a problem for this system and all the work is done automatically without any human intervention in identifying the best-suited resumes for the given job description. The CV extraction process consists of two phases. In the first phase, a CV is segmented into blocks based on their information type (Text Segmentation – Contact, Education and Experience). Here, the named entity recognition is applied to extract candidate information from the resumes. In the second phase, the resumes recommended by Top N are provided to the recruiter/employer based on the job description.
Issue
Design a model that can parse information from unstructured CVs and convert it into a structured format. Based on the job description, the model will recommend “top N” resumes from the extracted resumes to the employer/recruiter. Parse information from a resume using natural language processing and recommendation systems. Phase I: Manually classify a CV whether it is shortlisted for evaluation or not based on a particular job description Phase II: Train the model with the CVs based on the job description to classify whether it is selected or not Phase III: Recommend Top N CVs from the above -selected candidates for the given job description The accuracy of the model could be calculated by comparing the results of phase 3 with the manual selection performed in phase 1.
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