crawling, p arsing and semantic matching of vacancies and cv’s semantic recruitment technology...

Crawling, Parsing and Semantic Matching of Vacancies and CV’s

Semantic Recruitment Technology

Jakub Zavrel, TextkernelInGRID Workshop 11-2-2014

Textkernel: • Spinoff from R&D in machine learning and language

technology

• Founded 2001, offices in Amsterdam (HQ), Frankfurt, Paris, 45 employees; strong R&D focus

• Deloitte Fast 50 2007, 2010, 30% YoY growth

• Core technology: Understanding unstructured text data. Multi-lingual

Market:

• Job boards, Recruitment Software, Staffing and recruitment, Mobility, Large Employers

• Products:

• Multi-lingual tools (15 languages) to extract CVs and jobs

• Jobfeed: largest real time DB for job market analysis

• Search! & Match! to connect people and jobs

• Customers: UWV, Pole Emploi, Adecco, Randstad, USG, Monster, Stepstone, XING, SAP, Unisys, Bosch, Axa, Philips, etc. (350 direct, 2000+ indirect),

• Large partner network (HR & recruitment software)

I like programming, but I’m interested do take on more project management responsibility

Is there a job in our organisation that better fits my degree?

I’d like to work on our mobile strategy. I’ve helped a friend develop a mobile app.

I’d like to do more with my organisational talent.

We are looking to hire:An experienced tech team team lead

Language gap

The ideal candidate has:- min. 5yr of experience- Certfied scrummaster- Exp. w/iOS, Android

Completed academic studies Computer Science or related

30% travel for customer presentations

The Job ad searches directly in a database and identifies relevant candidates (or vice

versa) …

Automatically convert each document into a complete record

Extract! CV/Job Parsing

Extract!

Extract!

Extract!

Extract!

Extract! – Zero data entry job application

Extract!

• Time savings coding CVs and Jobs• If you accept noise, 100% time savings• Structured data allows better search:

Semantic Searching and Matching• Coding enables reporting and statistics

Extract!

• Coding follows Extraction• Customer specific or standard taxonomies• String similarity based normalization• Lot of synonyms per language• Distance = confidences • Problem cases: ambiguity, context, long tail• More complex models can help

(classifiers, multi-variate models)• Semantic matching better (occupation coding errors are

counterbalanced by other variables)

Occupation coding!

• Semantic search:

„Lets you find what you mean not what you type“

Impression...

Search!

Match!

Match!

Semantic Matching Technology:

• Natural Language Processing

• Machine Learning

• Semantic Analysis

• Probabilistic Language Model

• Search Engine

• Multi-lingual taxonomies

• Recruitment knowledge-bases

Demo

Search and analyse real-time online job ads as well as historical

data

Jobfeed

Jobfeed

Jobfeed!

Knowledge of all demand for labour in European job market

– Sales leads for recruitment and staffing companies– Real time labour market analytics tools– Largest database of jobs for matching unemployed– Perfect data source for text mining

Jobfeed!• Real time collection of online job ads from any

(unstructured) source

• Available in NL, DE, FR, IT• Gradually rolling out in rest of Europe• Richly semantically structured data

Jobfeed!

Jobfeed: Multilingual Occupation Taxonomy

Occupations >4000 codes4 languages3 layer hierarchy

>50K synonyms

Link to other concepts:- Skills- Education level- Sector- O*NET- UWV (Dutch Employment Agency)- ROME

Based on millions of jobs, years of customer feedback and experience!

Example: NL: administratief medewerker, EN: administrative assistant, FR: employé administratif, DE: Verwaltungsassistent (m/w).

Group: administrative personnelClass: Administration and Customer ServiceSynonyms: administrative employee, assistant clerk, office support

Skills: ms office, excel, english language, etc

O*NET: 43-9199.00: Office and Administrative Support Workers, All OtherUWV: 1000402563: Administratief medewerker secretariaat

Demo

Jobfeed as material for Research

Frequent words for "Java developer"

envandeeenjemetinhetJavaof

Jeopisvoorteervaringaanalsandsoftware

omteamzijnkennisbijErvaringdiethenaara

jaarjijbentDeveloperHBOhebttowerken

werk

Frequent words for all professions

envandeeeninhetjemetopJe

voorteisofzijnaanbentnaarbijom

alservaringdieHethebtdezewerkenzoekDewij

functieonzebentotoverwerkopleidinguitandwerkzaamheden

datbinnenuAlsVoorzelfstandigkennisooksverantwoordelijk

Solution: contrast frequencies

• Observed frequency of w: • O(w) = A• Expected frequency of w: • E(w) = C * B / D• Pick words with highest

score:• score(w) = (O - E)2 / E

Java develo

per jobs

Alljobs

# jobs where

w occurs

A B

Total # jobs C D

Top words for "Java developer"

javadevelopersoftwarespringscrumagilehibernateontwikkelaaruj2ee

developmentmavenapplicatieservaringwebdeframeworksjbossmbosenior

wijxmljeeojavascriptyoukennisontwikkelenoracleontwikkeling

architectuurwebservicesinformaticawerkzaamhedentechnologiedeveloperseclipsebezithetteam

worijbewijstechniekentomcatthevcazelfstandigarchitectwerklocatiehtml

Building rich skills profiles for thousands of occupations from millions of real time jobs…

… new trends and occupations…

Supply & Demand

• Have: lots of data, technology, ideas

• Want: labor market expertise, students, research

Semantic Recruitment Technology

Thanks!