Resume or Candidate screening is the way toward auditing employment forms or job applications. Screening resumes are the way toward arranging resumes to discover qualified or disqualified candidates utilizing progressively more detailed resume examinations. The resume screening objective decides whether a candidate is eligible for a role based on candidate education qualification, work experience, and other useful information by capturing from candidate’s resumes.
There are so many reasons to conduct screening of candidate resumes before recruitment for any company position or role. From those reasons a few important reasons when conducting resume screening.
Nowadays, the number of candidates for each job post is high, but the recruiter team is less. The workload on the recruiting team will be increased day by day. The manual resume screening process is time-consuming and needs high resources in terms of the recruiter’s efforts.
To avoid these consequences, AI-based models provide excellent solutions or results in the field of recruitment. Like that AI-Based solution also improves the resume screening, which is the most important task of recruiting.
Manually screening resumes is as yet the most time-consuming piece of recruiting, mainly when 60% to 80% of the resumes got for a job are unqualified. Screening resumes and shortlisting candidates to interview is assessed to take 23 hours of a recruiter’s time for a single candidate hiring process.AI-Based solutions can help recruiters in the hiring process if the solution can successfully automate time-consuming and repetitive tasks like a resume or candidate screening. As a little something extra, accelerating the way toward selecting through automation screening reduces time-to-recruit, which means organizations will be less inclined to lose the best talent to quicker moving competitors.
Information or data has gotten simpler to extract, gather, and analyze throughout the long term. Quality of recruiting has become the top prerequisite of the team of recruiters.The guarantee of AI for improving the nature of recruit lies in its capacity to utilize information to normalize the coordination between candidates’ work experience, education qualification, knowledge and skills, and the posting job’s necessities. This improvement in job matching is predicted by more productive employees who are less likely to turnover.
End of this data annotation process, extracted data will become more valuable data to our model. This means we can get crucial or essential points from every resume.
In this phase, the annotator will annotate different vital tags useful for extracting top resumes suitable for job posting based on their requirements, such as experience, skills, etc. For this, annotators will annotate the extracted text by different kinds of labels such as
Skills (such as domain knowledge), etc.
From annotated data, the model will learn all details of every candidate from their respective resumes. That will help models recognize or select top resumes from all based on requirements.
The process of screening resumes is automated by using Named entity recognition (NER). Named Entity Recognition (NER) is a subtask of Natural Language Processing (NLP) and Information Extraction (IE) that processes large amounts of unstructured human language to locate and classify named entities in text into predefined categories such as the names of persons, organizations, locations, etc. It is a supervised learning problem.
By using this named entity recognition technique, the model will identify all key or important features related to the candidate’s resumes. So automatically recruitment teams will be able to identify top priority resumes from a huge chunk of resumes. In this resume screening process named entity, recognition plays an important role.
The topic modeling technique is particularly useful for finding latent patterns in large collections of text by extracting clusters of words that are closely related and frequently occur together.
For example, in a database containing CVs from IT experts, programming skills may constitute a single topic.
There are so many algorithms available to build topic modeling techniques such as Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), etc. we can apply these techniques or algorithms to our huge corpus to identify the number of topics from that.We can group the different kinds of resumes based on topics that are important to the recruitment team for a particular job description.
After completing model development, we have to test that one. For simplicity, companies can deploy trained resume screening models with a front view such as different styles to look good. For this company can utilize already available deployment technologies such as web applications, docker images, etc. we can integrate the Rest API with web applications for a better view.After deployment, the recruiting team can be screening the resumes based on their job description. It makes their hiring process easy and effective by extracting the required information by named entity extraction. Automatically it reduces the cost of the hiring process. This process will provide potential candidates to organizations or companies.