The resume or CV is imported into parsing software and the information contained within the document file is extracted and distilled into its elements so that the data can be categorized according to predefined fields.
The extracted data is then automatically normalized. This means it is categorised according to a standard or customer specific format. Normalization ensures better searchability and analysis of the data processed.
Textkernel also offers her customers the ability to enhance their parsing offering with normalisation to a specific or even custom standard.
The O*NET Profession Classification– The O*Net professional code contains hundreds of standardized and occupation-specific descriptors of approximately 1,000 occupations covering the entire U.S. economy.
ISCO profession Classification – The International Standard Classification of Occupations (ISCO) is one of the main international classifications for which the International Labor Organization is responsible.
Textkernel Profession Classification – A classification including over 4,200 professions curated by Textkernel over the past 10 years thanks to access to over one billion job vacancies.
Textkernel Skills Classification – A classification carefully built and curated by Textkernel R&D, based on the analysis of millions of candidate documents and job vacancies processed. The Textkernel Skills Normalization Taxonomy currently contains about 135,000 terms that describe just over 11,000 skills, which are divided over four categories:
- Professional skills
- IT skills
- Soft skills
Machine learning is the technology that enables resume parsing. Thanks to hundreds of hours spent by human annotators across all our languages, large volumes of cvs are broken down into their component parts: personal and/or contact information, education, work experience, languages, etc. Then our algorithms are fed millions of cvs to ‘train’ and reinforce the patterns already deciphered by the human annotators.
Once the resume has been parsed, a recruiter can easily search their database for search terms required to generate a shortlist of relevant candidates. The Textkernel parsing software is essential for powering semantic search. Semantic search is a powerful search technology that adds context to the search terms and tries to understand intent in order to make the results more reliable and comprehensive. Now you can ensure that your recruiters don’t overlook potentially relevant candidates that might have otherwise been overlooked. Learn more about Textkernel’s Search! offering.
Key benefits of the Textkernel CV/Resume Parsing solution:
Dramatically less time required to process and shortlist candidates without compromising on result accuracy.
Our AI technology allows your recruiting team to focus on building human connections. The one thing that AI can never replace.
Our customers demand the highest quality results, otherwise the time savings gained through automation would be of little value. Textkernel continuously explores new techniques to optimise its extraction models.
Since 2017, Extract! has been powered by deep learning which has increased the parsing accuracy of even the most challenging cv formats. Learn more about how Textkernel was the first to launch Deep Learning to improve our parsing software quality.
Increase candidate conversion by incorporating our parsing at the very beginning of the candidate journey.
Our Extract! technology is not just a backend process. We have developed the ability to embed our parsing software into career portals and job sites. The benefits? Dramatically quicker and easier candidate application process that provides an improved candidate experience.
This improves your candidate experience while also giving your recruitment teams the ability to tailor the candidate journey based on their skills.
Textkernel’s highly accurate CV/resume parser, Extract!, supports recruiting organizations around the world to effectively and efficiently process large volumes of candidate documents.
Transform the millions of candidate applications into structured data that can be used to filter, search and rank candidates.