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.
"[7] One executive recruiting company tested three resume parsers and humans to compare the accuracy in data entry.
[8] In a 2012 experiment, a resume for an ideal candidate was created based on the job description for a clinical scientist position.
The parsing software has to rely on complex rules and statistical algorithms to correctly capture the desired information in the resumes.
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: With recent advancements in machine learning, the text mining and analysis processes, which ensure up to 95% accuracy in data processing, many AI technologies have sprung up to help the job seekers in the creation of application documents.
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.
This expansion to the search engine uses Cloud Talent Solution,[16] Google's own iteration of the AI resume builder and matching system.