Resume Parsing Definition
Resume parsing is a technology that uses natural language processing (NLP) to convert the unstructured text of a resume into a structured format. By accurately extracting information such as work experience, skills, and education, it streamlines the recruitment process and helps hiring teams focus on assessing the candidates’ qualifications.
Benefits of Resume Parsing
Resume parsing helps sift through the mountains of applications received for a single job opening, efficiently categorizing key data points, and making the information easily searchable. This tool undeniably saves time, standardizes processes, and allows for a more thorough analysis of applicants than its human counterparts alone.
Not only does Resume Parsing make it simpler for candidates and companies to create and save crucial data, but it also significantly enhances the search and display capabilities of applications. It bolsters your candidate-to-job matching precision and improve search results.
Who uses Resume Parsers
In our evolving world with Applicant Tracking Systems (ATS), Job Boards and social media, our Resume Parser stands as a vital intermediary between the application and a database. It works with resumes of all formats and file types, processing them individually or in bulk, outputting meticulously structured profile data.
The derived structured data can be used in various ways such as displaying the data in a user-friendly manner or saving it in a database. Recruitment agencies may remove the contact information and submit the candidate to a client for consideration. Once search engines like SOLR, Lucene, Elastic Search and others index the parsed data, the results delivered are accurate and context-specific. Companies also used the entire parsed context from the resume to find other resumes that are similar to that candidate and to compare the results to parsed Jobs.
How Resume Parsers and Job Parsers work together
The first step is to upload the resume into the resume parser. The parser will then read through the resume and extract the relevant data points. These data points can include things like work experience, education, skills, and accomplishments.
The second step is to upload the text of the job posting into the job posting parser. The parser will then read through the job posting and extract the relevant data points. These data points can include things like the job title, required qualifications, and desired characteristics. Once the relevant data has been extracted, it can be compared to the dataset generated by the resume parser.
The third step is to identify and match any candidates who appear in both the data generated by the resume parser and the data generated by the job posting parser. These are your top candidates for the given position.
About HireAbility’s Resume and CV Parser
HireAbility’s ALEX Resume Parser uses advanced AI techniques, including pattern recognition and semantic analysis, to extract relevant résumé/CV or job/vacancy data. The extracted information can range from the person’s name, contact, employment history, and education, to skills, awards, security clearances, hobbies, and more. Generally, the output of resume and job parsing is in XML or JSON format.
HireAbility’s ALEX Resume Parser is a game-changer, able to structure the most unconventional resumes. For instance, a search for Oracle as a skill will fetch candidates with Oracle skills, not ones working at Oracle without the skill.
In addition, HireAbility’s ALEX resume parser creates a summary of extracted data. The summaries include such data as the 3 most relevant competencies (most used over the years), the most recent company, competencies used in the most current job, highest education level, number of years in management positions, years of experience, security level and many more.