AI Powered Resume Ranker Solution – Overview

Artificial intelligence (AI) resume screening is the technique of sorting through resumes and applications to advance the most qualified individuals to the following stage of the hiring process.

What is an AI-based resume parser?

• Typically, resumes for candidates are sent to recruiters in Word or PDF format. The information is simple to read, but it is challenging to handle, especially if the recruiter receives hundreds of fresh resumes every day.
• The Resume parsing tool is useful in this situation. The recruiter is liberated from labour-intensive manual CV processing thanks to resume parsing technologies.
• An artificial intelligence-powered technology called a resume parser makes it possible to recognise and extract the relevant data from CVs in a variety of formats and present it in a clear and intelligible way.

How does the resume parsing algorithm work?

• All relevant applicant resumes are first uploaded into the parsing tool. If the solution offers such functionality, this can also be done automatically.
• The parser then goes through each document and extracts information pertinent to the needs of the recruiter and the applications, such as details on experience, skills, education, qualifications, and so forth.
• The procedure is automated by the Resume parsing software, which also offers the hiring managers with a list of qualified candidates without them having to spend countless hours manually screening each CV.

                                                                         Source: https://www.rchilli.com/

• So, you may wonder, “Do all resume parsers operate in the same manner?” No and yes. There can be some variations in how the information is taken from the resumes. There are just a handful resume parsing methods on the market right now:
• The text is searched for certain words, phrases, and patterns using a keyword-based resume parser. Though less precise, this method is the easiest. Due to confusing terms, the accuracy rate is just about 70%; the computer may not always correctly determine a word’s meaning.
• Technology supported by a grammar-based resume parser is more robust. It operates according to a set of grammatical norms. Each CV’s text information is broken down by the algorithm, which combines specific words and phrases to understand each sentence’s meaning.
• Grammar-based resume parsers’ accuracy is close to 90% (human accuracy, by the way, seldom exceeds 96%). Most of the time, parsers are able to comprehend the many meanings of words and phrases with ease and produce results that are more detailed.
• The most intelligent computing is statistical parsers. The information in resumes is analysed using numerical models. Along with discriminating between different word meanings, it can identify different types of structures, such as addresses, timelines, and so forth.
• The statistical parser needs to have been “trained” on the data it will be processing for it to be correct.
• The most recent intelligent resume parsing module we provided for one of our projects was a tailored version of Text kernel, a third-party Application for resume parsing.
• The system receives a list of CVs from applicants, which the recruiters post. Then, owing to pre-defined methods and interaction with an external tool, recruiters receive structured candidate profiles within the system. Because of this, talent acquisition specialists can quickly use an advanced search or sophisticated auto-matching algorithm to locate relevant applicants.

How we Developed an AI Powered Resume Ranker Solution Talent Acquisition Team

Solution Overview

• Resumes contain unstructured data. A pipeline is built to extract text from the resumes.
• The text is passed as input to the Universal Sentence Encoder and sentence embedding is created. The sentence embedding for job description is also created.
• The similarity between the two documents is found using cosine similarity

Technology

Market size: Resume parsing

Recruiting Software Market size was valued at USD 2,366.21 Million in 2021 and is projected to reach USD        3,873.98 Million by 2030, growing at a CAGR of 5.85% from 2023 to 2030.