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Resume parsing, also known as CV parsing, resume extraction, or CV extraction, allows for the automated storage and analysis of resume data. The resume is imported into parsing software and the information is extracted so that it can be sorted and searched.
Resume parsers analyze a resume, extract the desired information, and insert the information into a database with a unique entry for each candidate. [1] Once the resume has been analyzed, a recruiter can search the database for keywords and phrases and get a list of relevant candidates. Many parsers support semantic search, which adds context to the search terms and tries to understand intent in order to make the results more reliable and comprehensive. [2]
Machine learning is extremely important for resume parsing. Each block of information needs to be given a label and sorted into the correct category, whether that's education, work history, or contact information. [3] Rule-based parsers use a predefined set of rules to parse the text. This method does not work for resumes because the parser needs to "understand the context in which words occur and the relationship between them." [4] For example, if the word "Harvey" appears on a resume, it could be the name of an applicant, refer to the college Harvey Mudd, or reference the company Harvey & Company LLC. The abbreviation MD could mean "Medical Doctor" or "Maryland". A rule-based parser would require incredibly complex rules to account for all the ambiguity and would provide limited coverage.
Natural language processing (NLP) is a branch of artificial intelligence which uses machine learning to make predictions and to understand content and context. [5] Acronym normalization and tagging accounts for the different possible formats of acronyms and normalizes them. Lemmatization reduces words to their root using a language dictionary and stemming removes “s”, “ing”, etc. Entity extraction uses regular expressions, dictionaries, statistical analysis and complex pattern-based extraction to identify people, places, companies, phone numbers, email addresses, important phrases and more. [4]
Resume parsers have achieved up to 87% accuracy, [6] which refers to the accuracy of data entry and categorizing the data correctly. Human accuracy is typically not greater than 96%, so the resume parsers have achieved "near human accuracy." [7]
One executive recruiting company tested three resume parsers and humans to compare the accuracy in data entry. They ran 1000 resumes through the resume parsing software and had humans manually parse and enter the data. The company brought in a third party to evaluate how the humans did compared to the software. They found that the results from the resume parsers were more comprehensive and had fewer mistakes. The humans did not enter all the information on the resumes and occasionally misspelled words or wrote incorrect numbers. [8]
In a 2012 experiment, a resume for an ideal candidate was created based on the job description for a clinical scientist position. After going through the parser, one of the candidate's work experiences was completely lost due to the date being listed before the employer. The parser also didn't catch several educational degrees. The result was that the candidate received a relevance ranking of only 43%. If this had been a real candidate's resume, they wouldn't have moved on to the next step even though they were qualified for the position. [9] It would be helpful if a similar study was conducted on current resume parsers to see if there have been any improvements over the past few years.
The parsing software has to rely on complex rules and statistical algorithms to correctly capture the desired information in the resumes. There are many variations of writing style, word choice, syntax, etc. and the same word can have multiple meanings. The date alone can be written hundreds of different ways. [1] It is still a challenge for these resume parsers to account for all the ambiguity. Natural Language Processing and Artificial Intelligence still have a way to go in understanding context-based information and what humans mean to convey in written language.
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Resume parsers have become so omnipresent that it is now recommended that candidates focus on writing to the parsing system rather than to the recruiter. The following techniques have been proposed to increase the probability of success:
There are many stand-alone options for resume parsers including [16] RChilli, Skillate, CandidateZip, Sovren, Daxtra, Textkernel, Hireability and they are also typically bundled in with applicant tracking systems, which are used by companies to streamline the hiring process. [17]
With recent advancements in machine learning, the text mining and analysis processes, which ensure up to 95% accuracy [18] in data processing, many AI technologies [19] have sprung up to help the job seekers in the creation of application documents. These services focus on creating ATS-friendly resumes, execute resume check and screening, and help with all of the preparation and application processes. Some of the AI builders, such as Leap.ai and Skillroads, concentrate on the resume creation while others, like Stella, also offer help with the job hunt itself as they match candidates to appropriate vacancies. In 2017, Google launched Google for Jobs. This expansion to the search engine uses Cloud Talent Solution, [20] Google's own iteration of the AI resume builder and matching system.
Resume parsers are already standard in most mid- to large-sized companies and this trend will continue as the parsers become even more affordable. [12]
A qualified candidate's resume can be ignored if it is not formatted the proper way or doesn't contain specific keywords or phrases. As Machine Learning and Natural Language Processing get better, so will the accuracy of resume parsers.
One of the areas resume parsing software is working on expanding into is performing contextual analysis on the information in the resume rather than purely extracting it. One employee at a parsing company said “a parser needs to classify data, enrich it with knowledge from other sources, normalize data so it can be used for analysis and allow for better searching.” [21]
Parsing companies are also being asked to expand beyond just resumes or even LinkedIn profiles. They are working on extracting information from industry-specific sites such as GitHub and social media profiles. [21]
Optical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene photo or from subtitle text superimposed on an image.
An interview is a structured conversation where one participant asks questions, and the other provides answers. In common parlance, the word "interview" refers to a one-on-one conversation between an interviewer and an interviewee. The interviewer asks questions to which the interviewee responds, usually providing information. That information may be used or provided to other audiences immediately or later. This feature is common to many types of interviews – a job interview or interview with a witness to an event may have no other audience present at the time, but the answers will be later provided to others in the employment or investigative process. An interview may also transfer information in both directions.
A résumé, sometimes spelled resume, is a document created and used by a person to present their background, skills, and accomplishments. Résumés can be used for a variety of reasons, but most often they are used to secure new employment.
Staffing is the process of finding the right worker with appropriate qualifications or experience and recruiting them to fill a job position or role. Through this process, organizations acquire, deploy, and retain a workforce of sufficient quantity and quality to create positive impacts on the organization's effectiveness. In management, staffing is an operation of recruiting the employees by evaluating their skills and knowledge before offering them specific job roles accordingly.
Recruitment is the overall process of identifying, sourcing, screening, shortlisting, and interviewing candidates for jobs within an organization. Recruitment also is the process involved in choosing people for unpaid roles. Managers, human resource generalists, and recruitment specialists may be tasked with carrying out recruitment, but in some cases, public-sector employment, commercial recruitment agencies, or specialist search consultancies such as Executive search in the case of more senior roles, are used to undertake parts of the process. Internet-based recruitment is now widespread, including the use of artificial intelligence (AI).
An applicant tracking system (ATS) is a software application that enables the electronic handling of recruitment and hiring needs. An ATS can be implemented or accessed online at enterprise- or small-business levels, depending on the needs of the organization; free and open-source ATS software is also available. An ATS is very similar to customer relationship management (CRM) systems, but are designed for recruitment tracking purposes. In many cases they filter applications automatically based on given criteria such as keywords, skills, former employers, years of experience and schools attended. This practice of application filtering has caused many to adopt resume optimization techniques similar to those used in search engine optimization when creating and formatting their résumé.
A job interview is an interview consisting of a conversation between a job applicant and a representative of an employer which is conducted to assess whether the applicant should be hired. Interviews are one of the most common methods of employee selection. Interviews vary in the extent to which the questions are structured, from an unstructured and informal conversation to a structured interview in which an applicant is asked a predetermined list of questions in a specified order; structured interviews are usually more accurate predictors of which applicants will make suitable employees, according to research studies.
Yahoo HotJobs, formerly known as hotjobs.com, was an online job search engine. It provided tools and advice for job seekers, employers, and staffing firms. It was acquired by Yahoo in 2002, then acquired by Monster Worldwide, owner of its major competitor Monster.com in 2010—leading to its merger with Monster.com and eventual closure.
Sourcing is a talent acquisition discipline which is focused on the identification, assessment and engagement of skilled worker candidates through proactive recruiting techniques. Professionals specializing in sourcing are known primarily as Sourcers; but also Internet Recruiters, Recruiting Researchers or Talent Scouts.
Video résumé or video resume is a recording promoting a job seeker.
An application for employment is a standard business document that is prepared with questions deemed relevant by employers. It is used to determine the best candidate to fill a specific role within the company. Most companies provide such forms to anyone upon request, at which point it becomes the responsibility of the applicant to complete the form and return it to the employer for consideration. The completed and returned document notifies the company of the applicant's availability and desire to be employed as well as their qualifications and background so that a determination can be made as to the candidate's suitability to the position.
A talent community is a mechanism that employers use to keep active pipelines of talent for future recruitment. Talent pipelines consist of potential employees who are interested in working at a given employer, but are not ready to apply. This group is engaged with on a frequent basis so that when the potential employees are ready to seek a new job, they have a relationship with the company.
One-way interview, also known as asynchronous interview, pre recorded interview, virtual interview or digital interview, enables prospective employers to conduct online video interviews in an automated fashion. The interviews are conducted via websites or internet-enabled devices which use digital interviewing applications.
Entelo, Inc. is a privately held technology company located in San Francisco, California. Entelo offers a SaaS platform used by recruiters and hiring managers to find potential employees.
Recruitment marketing refers to the inbound strategies and tactics an organization uses to find, attract, engage, and nurture talent before they apply for a job, also called the pre-applicant phase of talent acquisition. It is the practice of promoting the benefits and value of working for an employer in order to recruit talent. It is analogous in many ways to corporate marketing, and is extremely similar to employer branding except recruitment marketing relates to trackable initiatives that drive awareness, engagement and conversion of applicants versus someone's impression of working at a company. Of course others see employer branding as a subset of recruitment marketing, in addition to extending the reach and exposure of career opportunities through search engine optimization (SEO), building and nurturing candidate relationships through talent communities, and the management of messaging and advertising of talent acquisition efforts.
A human resources management system (HRMS) or Human Resources Information System (HRIS) or Human Capital Management (HCM) is a form of Human Resources (HR) software that combines a number of systems and processes to ensure the easy management of human resources, business processes and data. Human resources software is used by businesses to combine a number of necessary HR functions, such as storing employee data, managing payroll, recruitment, benefits administration, time and attendance, employee performance management, and tracking competency and training records.
Algorithmic bias describes systematic and repeatable errors in a computer system that create "unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm.
Artificial intelligence (AI) in hiring involves the use of technology to automate aspects of the hiring process. Advances in artificial intelligence, such as the advent of machine learning and the growth of big data, enable AI to be utilized to recruit, screen, and predict the success of applicants. Proponents of artificial intelligence in hiring claim it reduces bias, assists with finding qualified candidates, and frees up human resource workers' time for other tasks, while opponents worry that AI perpetuates inequalities in the workplace and will eliminate jobs. Despite the potential benefits, the ethical implications of AI in hiring remain a subject of debate, with concerns about algorithmic transparency, accountability, and the need for ongoing oversight to ensure fair and unbiased decision-making throughout the recruitment process.
A social media background check is an investigative technique that involves scrutinizing the social media profiles and activities of individuals, primarily for pre-employment screening and other official verifications. These checks are performed to review people's online behavioral history on social media websites such as Facebook, Twitter, and LinkedIn. Social media background checks have become a common part of recruitment processes, among other verification procedures.
HireVue is an artificial intelligence (AI) and human resources management company headquartered in South Jordan, Utah. Founded in 2004, the company allows its clients to conduct digital interviews during the hiring process, where the job candidate interacts with a computer instead of a human interviewer.