Résumé parsing

resume optimizationRésumé parsers
Resume parsing, also known as CV parsing, resume extraction, or CV extraction, allows for the automated storage and analysis of resume data.wikipedia
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Résumé

resumeCVresumes
Resume parsing, also known as CV parsing, resume extraction, or CV extraction, allows for the automated storage and analysis of resume data.
Résumé parsers may correctly interpret some parts of the content of the résumé but not other parts.

Applicant tracking system

Applicant Tracking Systems
Resume parsers are also typically bundled in with Applicant Tracking Systems, which are used by companies to streamline the hiring process.
This has caused many to adapt resume optimization techniques similar to those used in search engine optimization when creating and formatting their résumé.

Parsing

parserparseparsed
The resume is imported into parsing software and the information is extracted so that it can be sorted and searched.

Semantic search

semantic search engineSemanticSemantic data store
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.

Machine learning

learningmachine-learningstatistical learning
Machine learning is extremely important for resume parsing.

Harvey Mudd College

Harvey MuddMuddHarvey-Mudd College
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.

Natural language processing

NLPnatural languagenatural-language processing
This leads us to Machine Learning and specifically Natural Language Processing (NLP).

Artificial intelligence

AIartificially intelligentA.I.
NLP is a branch of Artificial Intelligence and it uses Machine Learning to understand content and context as well as make predictions.

Text normalization

Acronym normalizationn11nNormalisation
Acronym normalization and tagging accounts for the different possible formats of acronyms and normalizes them.

Part-of-speech tagging

part of speech taggerpart-of-speechpart-of-speech tag
Acronym normalization and tagging accounts for the different possible formats of acronyms and normalizes them.

Lemmatisation

lemmatizationlemmatizedlemmatiser
Lemmatization reduces words to their root using a language dictionary and Stemming removes “s”, “ing”, etc. Entity extraction uses regex expressions, dictionaries, statistical analysis and complex pattern-based extraction to identify people, places, companies, phone numbers, email addresses, important phrases and more.

Stemming

word stemmingstemmedStemmer
Lemmatization reduces words to their root using a language dictionary and Stemming removes “s”, “ing”, etc. Entity extraction uses regex expressions, dictionaries, statistical analysis and complex pattern-based extraction to identify people, places, companies, phone numbers, email addresses, important phrases and more.

Named-entity recognition

named entity recognitionentity extractionnamed entities
Lemmatization reduces words to their root using a language dictionary and Stemming removes “s”, “ing”, etc. Entity extraction uses regex expressions, dictionaries, statistical analysis and complex pattern-based extraction to identify people, places, companies, phone numbers, email addresses, important phrases and more.

Regular expression

regular expressionsregexregexp
Lemmatization reduces words to their root using a language dictionary and Stemming removes “s”, “ing”, etc. Entity extraction uses regex expressions, dictionaries, statistical analysis and complex pattern-based extraction to identify people, places, companies, phone numbers, email addresses, important phrases and more.

Statistics

statisticalstatistical analysisstatistician
Lemmatization reduces words to their root using a language dictionary and Stemming removes “s”, “ing”, etc. Entity extraction uses regex expressions, dictionaries, statistical analysis and complex pattern-based extraction to identify people, places, companies, phone numbers, email addresses, important phrases and more.

LinkedIn

Linked InLinkedIn PulseLinkedIn Corp.
Since the technology has already gotten so efficient, many companies are allowing applicants to apply just using their LinkedIn profile.

Fortune 500

Fortune 100Fortune'' 500Fortune 500 companies
90% of Fortune 500 companies use Applicant Tracking Systems and they can do everything from processing job applications, managing the recruiting process and executing the hiring decision.

Text mining

text analyticstext-miningtext
With recent advancements in AI sophistication and Machine Learning, and the text mining and analysis processes improvements, which ensure up to 95% accuracy in the data processing, many AI tecnologies have sprung up to help the job seekers in the creation of application documents.

Job hunting

job seekersjob searchjob applicants
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.

GitHub

github.comGistOctocat
They are working on extracting information from industry-specific sites such as GitHub and social media profiles.