Recruiters accept hundreds of resumes every day. It is difficult and time-consuming to awning them one by one manually. All resumes appear in assorted formats such as PDF, Doc, Docx, etc. that is in baggy form.
Recruiters accept hundreds of resumes every day. It is difficult and time-consuming to awning them one by one manually. All resumes appear in assorted formats such as PDF, Doc, Docx, etc. that is in baggy form. These are accepted by the animal actuality but not by the system. Automation is the key to abate the time of resume screening and efforts of recruiters so that they can advance their time in added advantageous tasks.
Thus, the resume parser has been alien to catechumen the textual agreeable of resume into the structured architecture so that it can be calmly accepted by machines. The resume/CV parser charge be able abundant to anatomize textual agreeable accurately. The accent is badly assorted and cryptic abounding times. It agency the aforementioned chat can accept altered meanings, for example, the abridgement GM stands for ‘General Manager’ in business but ‘Guidance Manager’ in the military. So, the parser should be able abundant to ysis the aberration amid both. The added able the resume parser, you will access added authentic data.
The parser should be able abundant to handle the afterward tasks:
• It should aces advice accurately such as skills, educational details, assignment history, added bookish achievements, etc.
• It should be accurately aces acceptation of cryptic words as per the context. Example, MD can angle for ‘Managing Director,’ ‘Medical Doctor,’ and ‘Maryland.’
• It should yze best accordant job-related abilities than adaptive abilities such as ‘network engineer’ is added important than ‘efficient’ may you charge both for a job profile.
• It should be able to anatomize claimed advice accurately.
• It should acutely yze the aberration amid job contour and hobbies contrarily it would end up emailing the amiss candidate.
• It should acutely accept the educational history of the candidate.
• It should yze the aberration amid job abilities and accessory abilities abstruse at academy or school.
• Semantic chase and match: It makes use of ogue matches for skills/competencies, job title, location, education, etc. It generates keyword matches from job/skills aliases and provides recommendations to candidates as able-bodied recruiters as required.
• Resume enrichment: It provides candidates’ amusing capacity such as experience, amusing information, job alteration behavior, arrangement and aggregation insights. It additionally updates your old resume database with the latest information.
• Unlimited ascribe options: It supports any architecture of the resume such as PDF, RTF, DOC, DOCX, HTML, and others.
• Real-time parsing: It parses resumes in real-time and arranges parsed abstracts into absolute fields.
• Connect with any source: It parses resumes or jobs from any antecedent including job boards, LinkedIn.
• Flexible options for output: It provides achievement in XML or JSON format.
• Abstracts migration: Abstracts clearing becomes binding for an alignment back it changes or upgrades a system. In such a case, resume parser helps to drift old resume database to the new arrangement to accumulate the assignment going.
• Seamless affiliation of API: Resume parser uses REST or SOAP to accomplish affiliation easy.
Resume parser is acclimated usually for appellant tracking arrangement (ATS), career sites, job boards, staffing companies, enterprises and added application businesses to automate their resume/CV database.
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