Textkernel talks - introduction to Textkernel

•Download as PPTX, PDF•

1 like•1,450 views

by Darko Zelić, Software Engineer at Textkernel. Textkernel organises monthly Textkernel Talks; technical and practical presentations from research and industry specialists. Topics can include Topics involve NLP, IR, Deep Learning, Semantic Search, LTR and more. This presentation was held at the first event on Thursday 3 September. Join the Textkernel Talks meetup group (http://www.meetup.com/textkernel-talks/) to stay informed of all events. Ideas for meetup events at Textkernel? Contact us via talks@textkernel.nl.

About Textkernel
• Founded in 2001, currently 65 employees
• R&D spin-off from research in Natural Language Processing and Machine
Learning at the Universities of Tilburg, Antwerp and Amsterdam
• Specialised in information extraction, web mining and semantic searching &
matching in the HR industry

About Textkernel Talks
• Technical and practical presentations from research and industry specialists
• Topics: NLP, IR, deep learning, semantic search, LTR, ...
• Previously: (semi-)private events, irregular frequency
• Plan: public monthly events (our meetups + hosting other meetup groups)
• Ideas? → talks@textkernel.nl

About me
• Software Engineer at Textkernel since January 2012.
• Working on core products and on new prototypes / innovation ideas
• MSc in Computer Science @ FTN, Novi Sad, Serbia
• BSc (hons) in Software Engineering @ RAF, Belgrade, Serbia
• hackathons, craft beer, digital photography and black metal
• Find me at <almostAnySocialNetwork>/dachaz

• A small overview of Textkernel’s products
• Boaz Leskes talks about Resiliency in Elasticsearch
Today

• A small overview of Textkernel’s products
• Boaz Leskes talks about Resiliency in Elasticsearch
• pizza and beer! (and sodas)
Today

Harvester
Extract!
Sourcebox Match!
JobfeedSearch!
Empowering recruitment

Textkernel products in a recruitment process

Automatically converts resumes into complete candidate records
Extract! CV Parsing
Sourcebox
Document workflow, automating the bulk of application processes

• A positive candidate experience (quicker application process)
• Time saving when entering resumes
• Structured data allowing better search
Extract! & Sourcebox

Easily, effectively and efficiently find candidates in multiple databases and
social media powered by Elasticsearch
Search!

Auto suggest
Search with
synonyms
Semantic search

• User-friendly interface
• Simultaneously searching multiple databases and social media
• Save external profiles quickly and easily
Search!

Automatically get an overview of relevant candidates for a given job and vice
versa
Match!

Upload a job posting
and automatically
get a search query
Search with a job ad

• Automatic, fast and relevant
• Transparent search
• No tedious and expensive query formulation
• Fast and cost-effective way to fill positions
Match!

Search and analyse real-time online job ads as well as historical data
powered by Elasticsearch
Jobfeed

Jobfeed
Search all online jobs by
criteria like organisation,
intermediary and sector

A job from
Jobfeed is
converted into a
query
and matched to
the candidate
database
Match integration

Automatic collecting and publishing of job ads from customer’s websites onto
job boards
Harvester

Why did you tell me all of this?
• Giving you a (vague) idea about our products and technologies
→ hint what you can expect on future meetups

Why did you tell me all of this?
• Giving you a (vague) idea about our products and technologies
→ hint what you can expect on future meetups
• We’re hiring!
→ http://www.textkernel.com/jobs/

Textkernel talks - introduction to Textkernel

Dr. Gábor Kismihók's presentation at Textkernel's Conference Intelligent Machines and the Future of Recruitment on 2 June 2016. Learning analytics is an emerging discipline in education, aiming at analysing (big) educational data in order to improve learning processes. In this talk, Dr. Gábor Kismihók will give an overview about the main challenges of this field, with a special emphasis on bridging the education - labour market divide.

Set the Hiring Managers’ Expectations: Using Big Data to answer Big Questions...

Textkernel

Presentation by Abdel Tefridj at Textkernel's Conference Intelligent Machines and the Future of Recruitment on 2 June 2016 in Amsterdam. Abdel shares some client scenarios when data was the key element in the decision making process for recruitment challenges. You can become a better partner with hiring managers when they are informed about the latest trends in the marketplace using supply, demand and compensation data. Learn how to use big data to make you a stronger leader and contributor.

atsearch crowd recruitment

Elena Morozova

EE to Data Science - Why and How of the Pivot

Irfan Elahi

Could You be a Data Scientist? Quantify Data Scientist Profiles using Machine...

Carlo Torniai

Loras College 2016 Business Analytics Symposium Keynote

Rich Clayton

Leaders who embrace data have a profound impact on their organizations yet too few seize the opportunity. Biases in decision making, technology myths, data quality and analytical skills and are the most frequently cited obstacles by organizations of all sizes. Technology advances have neutralized the scale advantage and have democratized analytics for every organization – so now what? Are you to engage more data in your management decisions? Do you have an analytic strategy that has two speeds – one for innovation and one for scale? Are you investing in your top talent so they can ask new questions? We’ll explore these topics and how to create an analytic culture in your organization. We’ll share how leaders have transformed their organizations by innovating their analytic processes, re-designing the way they work and embracing new technology innovation. We’ll dispel myths about technology and provide you a foundation for building your journey to analytic excellence.

1 data science with python

Vishal Sathawane

Lean Analytics is a set of rules to make data science more streamlined and productive. It touches on many aspects of what a data scientist should be and how a data science project should be defined to be successful. During this presentation Richard will present where data science projects go wrong, how you should think of data science projects, what constitutes success in data science and how you can measure progress. This session will be loaded with terms, stories and descriptions of project successes and failures. If you're wondering whether you're getting value out of data science, how to get more value out of it and even whether you need it then this talk is for you! What you will take away from this session Learn how to make your data science projects successful Evaluate how to track progress and report on the efficacy of data science solutions Understand the role of engineering and data scientists Understand your options for processes and software

How to build a data science team 20115.03.13v6Zhihao Lin

KDD 2019 IADSS Workshop - Research Updates from Usama Fayyad & Hamit Hamutcu

IADSS

Data Culture Series - Keynote - 24th feb

Jonathan Woodward

Big data. Small data. All data. You have access to an ever-expanding volume of data inside the walls of your business and out across the web. The potential in data is endless – from predicting election results to preventing the spread of epidemics. But how can you use it to your advantage to help move your business forward? Data is growing exponentially and it’s now possible to mine and unlock insights from data in new and unexpected ways. Empower your business to take advantage of this data by harnessing the rich capabilities of Microsoft SQL Server and the familiarity of Microsoft Office to help organize, analyze, and make sense of your data—no matter the size.

The Big Data Dream Team

Accenture Analytics

Jeff Moss Bio On A Page v 1.3

Jeff Moss

KDD 2019 IADSS Workshop - Standardizing data science to help hiring - Greg Ma...

IADSS

NDC Oslo : A Practical Introduction to Data Science

Mark West

Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all this? In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections: (1) I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation. (2) Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied. (3) The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.

The Technology Horizon: Implications for assessment

Phil Marston

HighPredict Technical Introduction 2018

Efi Jeremiah

Digital Economics

Lee Schlenker

Jaideep sai k

Jaideep Sai

What companies hiring data scientists and hadoop developers are looking for?

DeZyre

Webinar search! 2.1 english textkernel

Textkernel

This is the presentation used at the Webinar: Product-presentation Search! 2.1 by Gerard Mulder of Textkernel. Search! is the semantic recruiting tool of Textkernel that makes it easier to find the perfect candidate, either in your own database or in external sources. This presentation contains screen shots of some of the new features such as: - Auto-suggestions - Saving projects, searches and results - Creating e-mail alerts For more information visit www.textkernel.com

Keyword research tools for Search Engine Optimisation (SEO)

Duncan MacGruer

Behavior Driven Development

NETUserGroupBern

From SQL to Python - A Beginner's Guide to Making the Switch

Rachel Berryman

SEO Do's and Dont's - Search in 2018

Linus Logren

SCONUL Summer Conference 2019 - Svein Arne Brygfjeld

sconul

Artificial intelligence @ the National Library of Norway - Svein Arne Brygfjeld, National Library of Norway Svein Arne highlighted the work of the National Library of Norway and how one of their latest projects is successfully converting analogue media to digital form, while integrating different media collections together so that users can discover and access a range of resources on specific topics online.

Infoventure presentation Elasticsearch meet up

DianaGoebel

What's hot

Making a Systematic Business Case for Analytics

Ashwin Malshe

Data Science-Why?What?How? By Hari Prasad

Hari Prasad

Lean Analytics: How to get more out of your data science team

Digital Transformation EXPO Event Series

How to build a data science team 20115.03.13v6Zhihao Lin

KDD 2019 IADSS Workshop - Research Updates from Usama Fayyad & Hamit Hamutcu

IADSS

Data Culture Series - Keynote - 24th feb

Jonathan Woodward

The Big Data Dream Team

Accenture Analytics

Jeff Moss Bio On A Page v 1.3

Jeff Moss

KDD 2019 IADSS Workshop - Standardizing data science to help hiring - Greg Ma...

IADSS

NDC Oslo : A Practical Introduction to Data Science

Mark West

The Technology Horizon: Implications for assessment

Phil Marston

HighPredict Technical Introduction 2018

Efi Jeremiah

Digital Economics

Lee Schlenker

Jaideep sai k

Jaideep Sai

What companies hiring data scientists and hadoop developers are looking for?

DeZyre

What's hot (15)

Making a Systematic Business Case for Analytics

Data Science-Why?What?How? By Hari Prasad

Lean Analytics: How to get more out of your data science team

How to build a data science team 20115.03.13v6

KDD 2019 IADSS Workshop - Research Updates from Usama Fayyad & Hamit Hamutcu

Data Culture Series - Keynote - 24th feb

The Big Data Dream Team

Jeff Moss Bio On A Page v 1.3

KDD 2019 IADSS Workshop - Standardizing data science to help hiring - Greg Ma...

NDC Oslo : A Practical Introduction to Data Science

The Technology Horizon: Implications for assessment

HighPredict Technical Introduction 2018

Digital Economics

Jaideep sai k

What companies hiring data scientists and hadoop developers are looking for?

Similar to Textkernel talks - introduction to Textkernel

Webinar search! 2.1 english textkernel

Textkernel

Keyword research tools for Search Engine Optimisation (SEO)

Duncan MacGruer

Behavior Driven Development

NETUserGroupBern

From SQL to Python - A Beginner's Guide to Making the Switch

Rachel Berryman

SEO Do's and Dont's - Search in 2018

Linus Logren

SCONUL Summer Conference 2019 - Svein Arne Brygfjeld

sconul

Infoventure presentation Elasticsearch meet up

DianaGoebel

SG_UserGroup_Oct20_2022_NLP_AzureLangStudio.pptx

PriyankaShah668821

How To Implement Engineering Search Within Your Organization Webinar

Concept Searching, Inc

What if you could not only search and discover, but also analyze, visualize and apply artificial intelligence to a normalized set of all structured and unstructured content within your organization, securely and in near real time? Concept Searching, C/D/H, and Microsoft have partnered to bring you a global, cross-industry engineering search solution to deliver an unprecedented, unified view of all content, which your organization can rely on to grow and thrive. Most organizations typically take twelve months or more to implement. We typically implement in two months – from scratch and hyper-agile. This short How To webinar demonstrates our global cross-industry engineering search solution which delivers an unprecedented and unified view of all content within your organization. • Fast – Find everything within your organization within three seconds • Secure – Returns only results each person already has access to • Normalized – based on a corporate governed nomenclature map • Search – Concepts, compound terms, ranges, and normalized semantics • Content – Both structured and unstructured • Sources – From file shares, LOB systems, databases, websites • Visualized – Full preview of all types of content for visual detection • Organized – Refinement based on corporate taxonomy • Analyzed – Applied artificial intelligence for predictive analytics

Textkernel Talks - Neo4j usage in Textkernel

Textkernel

by Alexey Shevchenko, PHP developer at Textkernel. Textkernel organises monthly Textkernel Talks; technical and practical presentations from research and industry specialists. Topics can include Topics involve NLP, IR, Deep Learning, Semantic Search, LTR and more. This presentation was held at the joint event with GraphDB Meetup on Wednesday 9 December. Join the Textkernel Talks meetup group (http://www.meetup.com/textkernel-talks/) to stay informed of all events. Ideas for meetup events at Textkernel? Contact us via talks@textkernel.nl.

Content Discovery Through Entity Driven Search

Alessandro Benedetti

Leveraging enterprise information is no easy task, especially when unstructured information represents more than 80% of enterprise content. Meaningfully structuring content is critical for companies, Natural Language Processing and Semantic Enrichment is becoming increasingly important to improve the quality of tasks related to information retrieval. With the Semantic Web moving towards full realisation thanks to the Linked Data initiative and with the interest of major search engines in structured data, the enterprise search world is finding it more attractive to make its information machine readable and exploit that information to improve search over its content. In this scenario, three trends are transforming the face of search: Entity-oriented search. Searching not by keyword, but by entities that represent specific concepts in a certain domain. Knowledge graphs. Leveraging relationships amongst entities: Linked Data datasets (Freebase, DbPedia….) or custom companies’ knowledge bases. Search assistance. Autocomplete and spellchecking are now common features, but making use of semantic data makes it possible to offer smarter features, guiding the users to what they want, in a natural way. Sometimes, the proper resources for building such features are not easy to obtain. In order to generate these, our approach includes a number of unstructured data processing mechanisms the goal of which is to automatically extract semantic information: Extract content from heterogeneous data sources Extract domain information and enrich the content through different NLP processes like Named Entity Recognition, Coreference Resolution, Entity Linking and Disambiguation, and Topic Annotation Create specialised indexes to store the semantic information extracted Currently there are a number of well developed uses of semantic extracted information such as faceting and concept indexing, however further methods of exploiting semantic extracted information are presenting themselves in the industry: Smart Autocomplete The target of this feature is to automatically complete users’ phrase with entity names and properties, helping them to find the desired documents through exploration of the domain Knowledge Graph. As the user keys in the phrase, the system will propose a set of named entities and/or a set of entity types. As the user accepts a suggestion, the system will dynamically adapt following suggestions to the chosen context. The accuracy delivered by entity driven search brings increased satisfaction among users. They will see documents that are about a specific semantic concept, with concrete properties, and not about a keyword that can be ambiguously interpreted. Semantic More Like This A feature to find documents similar to one that is input, based on the underlying knowledge in the documents, instead of tokens.

ECIR-2014: Multilanguage Content Discovery Through Entity Driven Search

Antonio David Pérez Morales

The evolution of Search spscinci

Johnny Lopez

AI-SDV 2021: Stefan Geissler - AI support for creating and maintaining vocabu...

Dr. Haxel Consult

Structured vocabularies, thesauri and lexicons are key ingredients for many information management tasks. Creating them however often requires a significant amount of work. Maintaining and extending them often means that the respective manual tasks need to be done on a regular basis in order to prevent the resources from becoming outdated, irrelevant and incomplete. AI has much support to offer for this task. And by wrapping the respective approaches into applications that can be operated by terminologists and domain experts who don't need to be programmers or data scientists themselves, the benefits can be made available to a wide range of users.

Designing and Implementing Search Solutions

Findwise

THAT Conference 2021 - State-of-the-art Search with Azure Cognitive Search

Brian McKeiver

Starting a career in data science

Brian Spiering

Semtech bizsemanticsearchtutorial

Barbara Starr

RecSys 2015 Tutorial - Scalable Recommender Systems: Where Machine Learning m...

Joaquin Delgado PhD.

This tutorial gives an overview of how search engines and machine learning techniques can be tightly coupled to address the need for building scalable recommender or other prediction based systems. Typically, most of them architect retrieval and prediction in two phases. In Phase I, a search engine returns the top-k results based on constraints expressed as a query. In Phase II, the top-k results are re-ranked in another system according to an optimization function that uses a supervised trained model. However this approach presents several issues, such as the possibility of returning sub-optimal results due to the top-k limits during query, as well as the prescence of some inefficiencies in the system due to the decoupling of retrieval and ranking. To address this issue the authors created ML-Scoring, an open source framework that tightly integrates machine learning models into Elasticsearch, a popular search engine. ML-Scoring replaces the default information retrieval ranking function with a custom supervised model that is trained through Spark, Weka, or R that is loaded as a plugin in Elasticsearch. This tutorial will not only review basic methods in information retrieval and machine learning, but it will also walk through practical examples from loading a dataset into Elasticsearch to training a model in Spark, Weka, or R, to creating the ML-Scoring plugin for Elasticsearch. No prior experience is required in any system listed (Elasticsearch, Spark, Weka, R), though some programming experience is recommended.

RecSys 2015 Tutorial – Scalable Recommender Systems: Where Machine Learning...

S. Diana Hu

Search engines have focused on solving the document retrieval problem, so their scoring functions do not handle naturally non-traditional IR data types, such as numerical or categorical. Therefore, on domains beyond traditional search, scores representing strengths of associations or matches may vary widely. As such, the original model doesn’t suffice, so relevance ranking is performed as a two-phase approach with 1) regular search 2) external model to re-rank the filtered items. Metrics such as click-through and conversion rates are associated with the users’ response to items served. The predicted selection rates that arise in real-time can be critical for optimal matching. For example, in recommender systems, predicted performance of a recommended item in a given context, also called response prediction, is often used in determining a set of recommendations to serve in relation to a given serving opportunity. Similar techniques are used in the advertising domain. To address this issue the authors have created ML-Scoring, an open source framework that tightly integrates machine learning models into a popular search engine (SOLR/Elasticsearch), replacing the default IR-based ranking function. A custom model is trained through either Weka or Spark and it is loaded as a plugin used at query time to compute custom scores.

Similar to Textkernel talks - introduction to Textkernel (20)

Webinar search! 2.1 english textkernel

Keyword research tools for Search Engine Optimisation (SEO)

Behavior Driven Development

From SQL to Python - A Beginner's Guide to Making the Switch

SEO Do's and Dont's - Search in 2018

SCONUL Summer Conference 2019 - Svein Arne Brygfjeld

Infoventure presentation Elasticsearch meet up

SG_UserGroup_Oct20_2022_NLP_AzureLangStudio.pptx

How To Implement Engineering Search Within Your Organization Webinar

Textkernel Talks - Neo4j usage in Textkernel

Content Discovery Through Entity Driven Search

ECIR-2014: Multilanguage Content Discovery Through Entity Driven Search

The evolution of Search spscinci

AI-SDV 2021: Stefan Geissler - AI support for creating and maintaining vocabu...

Designing and Implementing Search Solutions

THAT Conference 2021 - State-of-the-art Search with Azure Cognitive Search

Starting a career in data science

Semtech bizsemanticsearchtutorial

RecSys 2015 Tutorial - Scalable Recommender Systems: Where Machine Learning m...

RecSys 2015 Tutorial – Scalable Recommender Systems: Where Machine Learning...

More from Textkernel

Textkernel Emerce eRecruitment - 6 april 2017

Textkernel

Maakt Artificial Intelligence het werk van de recruiter straks overbodig? In tegendeel! In een krapper wordende arbeidsmarkt wordt recruitment en tijd voor de kandidaat steeds belangrijker. In deze presentatie geven we een korte introductie over AI en laten we zien waarom het juist voor recruitment belangrijk is en hoe het je helpt beter te sourcen en te matchen. We sluiten af met interessante klantcases van o.a. USG People en CERN.

Practical Deep Learning for NLP

Textkernel

AI Reality: Where are we now? Data for Good? - Bill Boorman

Textkernel

Robots Will Steal Your Job but That's OK - Federico Pistono

Textkernel

Presentation of researcher and entrepreneur Federico Pistono, author of "Robots Will Steal Your Job, But That's OK", that was held at Textkernel's conference Intelligent Machines and the Future of Recruitment on June 2nd in Amsterdam. At the end of this slide deck, you can also find the YouTube recording. Outline: Over the past four years, headlines warned us that a wave of joblessness is coming. They claim that advances in robotics, machine learning, and automation are ushering in an era of unprecedented change. Do these concerns reflect reality? Some claim that we have seen this story before, and that we have nothing to worry about. Others think that this time is different, and that we're about to experience the most dramatic shift in modern economic history, one for which we are not prepared. But what is the real risk of technological unemployment? How will it affect the job market, recruitment, and the economy at large? In this presentation, Federico Pistono separated the myths from reality by presenting the state of the art and forecasts of machine intelligence and its economic impact.

Semantic Interoperability in the Labour Market - Martin le Vrang, Team leader...

Textkernel

This presentation was held by Martin le Vrang at Textkernel's conference Intelligent Machines and the Future of Recruitment on 2 June in Amsterdam. The European Commission is developing a multilingual classification of European Skills, Competences, Qualifications and Occupations (ESCO). This common reference terminology will enhance the functioning of the labour market, help to build an integrated European labour market and bridge the communication gap between work and education/training. ESCO is part of an emerging Semantic Web in the labour market and the education and training sector. Job vacancies, CVs and training curricula would no longer just be documents, but standardised sets of data which can be reused in job matching, HR systems, for career guidance tools or in statistical applications.

Pablo de Pedraza: Labor market matching, economic cycle and online vacancies

Textkernel

Pablo de Pedraza's presentation at Textkernel's Conference Intelligent Machines and the Future of Recruitment on 2 June 2016. The number of job openings, or vacancies, is an important indicator of the state of the economy and the labour market. They are extensively used by institutions and in academic papers to calculate the Beveridge Curve or estimate the matching function, center pieces of macroeconomic models studying labor markets. Vacancies can be measured using administrative registers, surveys to employers, advertisements in printed press or using online advertising. This presentation is divided into two sections. In the first one we study the Dutch Beveridge curve and the matching function using the number of vacancies inferred from a survey to employers conducted by the Dutch Central Bureau of Statistics (CBS) from 1997 until the end of 2014. We obtain conclusion about matching process before and after the Great Recession. In the second section we compare number of vacancies inferred from CBS vacancy data with the number of vacancies posted online. According to CBS data, the number of vacancies increases during positive shocks and goes down during negative ones. We can observe the number of web vacancies posted online from 2006 until today and compare them with CBS data during a complete economic cycle. Results show a positive time trend in the number of online vacancies and negative time trend in the number of vacancies inferred from a survey. We show that both series reflect very similar economic reality once we account for both trends. We settle our future research lines focusing on exploring the sources behind both trends and how they compare across sectors.

New Developments in Machine Learning - Prof. Dr. Max Welling

Textkernel

Presentation from Prof. Dr. Max Welling, Professor of Machine Learning at the University of Amsterdam, at Textkernel's Intelligent Machines and the Future of Recruitment on June 2nd in Amsterdam. At the end of this slide deck, you can also find the YouTube recording. Due to increased compute power and large amounts of available data, machine learning is flourishing once again. In particular a technology called deep learning is making great strides maturing into a powerful technology. Max Welling briefly discusses variants of deep learning, such as convolutional neural networks and recurrent neural networks. But what lies around the corner in machine learning? He will discuss the three developments that in his opinion will become increasingly important: 1) Learning to interact with the world through reinforcement learning, 2) Learning while respecting everyone's privacy, and 3) Learning the causal relations in data (as opposed to discovering mere correlations). Together, they represent the "power tools" of the future machine learner.

The Agile Future of HR and Talent Acquisition - Prof. Dr. Armin Trost

Textkernel

Presentation from Prof. Dr. Armin Trost, Author, Consultant and Professor at Furtwangen University, at Textkernel's Intelligent Machines and the Future of Recruitment on June 2nd in Amsterdam. At the end of this slide deck, you can also find the YouTube recording. Human resource management in the 21st century will have little to do with what has been promoted in recent years or decades and written in the text-books. Instead of finding “the right people, at the right time and at the right place” we will make the employees and their individual preferences, talents, life plans, and ambitions the focus of attention. We will say goodbye to mechanistic, technocratic, and often bureaucratic approaches. They fit in a past that was stable and predictable. If you regard your employees as your most valuable asset, you will give them freedom, trust, and responsibility. Moreover you will appreciate individuality and individual life-plans. Human resources management will therefore deal less with hierarchical processes, systems, responsibilities, KPIs, etc., in the future. Rather, it will be about how to empower teams to think on their own responsibility, communicate, collaborate, learn, and develop their talent in the long term. HR-Technology will be there to make the life of managers and employees easier instead of supporting the HR-function only. For instance, in the area of recruiting all this will lead to a more intense usage of social networks, artificial intelligence, big data, data mining etc.

Human > Machine Interface - The future of HR | Perry Timms, Founder & Directo...

Textkernel

Presentation by Perry Timms at Textkernel's conference Intelligent Machines and the Future of Recruitment on 2 June 2016 in Amsterdam. With a spotlight on AI; VR/AR; robotics, automation, machine learning and quantum computing, what does this mean for the world of work, jobs and human endeavour? More so, what does it mean to the technophobia often present in HR? There’s a thought that HR doesn’t even really get the technology that’s being used now and is having a profound effect on where, when and how people are working. And no, self-service cloud-based HR systems doesn’t mean the profession is anywhere near to being tech savvy. That’s low level labour realignment and marginal process improvement. My fear - as an HR professional aware of and experimenting with technology constantly - is that my profession is already WAY BEHIND the curve so how will HR practitioners cope with the latest array of digital disruption? Join me in finding out how I believe we can upgrade HR’s thinking and doing for the digital age of work.

Ton Sluiter: Breaking Barriers and Leveraging Data

Textkernel

Ton Sluiter's presentation at Textkernel's conference Intelligent Machines and the Future of Recruitment that took place on 2 June 2016 at the Beurs van Berlage in Amsterdam. In this presentation Ton Sluiter discusses the way CV Search! from Textkernel has contributed to make the candidate databases of Star Brands and USG People accessible to one another. Furthermore, he takes a look at the extra insights USG People has gained from the parsed CVs.

How semantic search changes recruitment - Glen Cathey

Textkernel

Presentation by Glen Cathey, SVP Talent Strategy and Innovation at Kforce, at Textkernel's conference Intelligent Machines and the Future of Recruitment on 2 June at the Beurs van Berlage in Amsterdam. At the end of this slide deck, you can also find the YouTube recording. Without semantic search, recruiters searching for potential candidates only see a fraction of available and relevant results and unknowingly exclude qualified candidates unless they understand and employ advanced methods of manual information retrieval. In this keynote, Glen Cathey will explain how semantic search has specifically impacted recruitment today and how further advancements will impact recruitment in the future.

The Role of Public Innovation and the Impact of Technology on Employment - Re...

Textkernel

Presentation by Reynald CHAPUIS, Director of innovation and Corporate Social Responsibility at Pôle Emploi, at Textkernel's conference Intelligent Machines and the Future of Recruitment that was held on 2 June 2016 at the Beurs van Berlage in Amsterdam. Reynald Chapuis presents Pôle emploi, a key player in the French Public Employment Service, and its innovation system through multiple and collaborative platforms. He presents 3 case studies on how Pôle emploi uses data, artificial intelligence and machine learning for the benefit of job advisors and jobseekers.

It’s all about Technology... oh wait! It’s not - Balazs Paroczay

Textkernel

Presentation from Balazs Paroczay, Head of Recruiting Strategy and Innovations, Randstad Sourceright EMEA, at Textkernel's conference Intelligent Machines and the Future of Recruitment on 2 June in Amsterdam. Due to digital technology revolution, sourcing for good candidates is basically not a challenge anymore. There are search plugins but also productivity tools, document and data-grabbing, parsing and matching, email verification, image search and soon-coming face recognition applications, click-rate or any-other-type data analytics softwares (trillions of them!), and it looks like the core competitive advantage of a top sourcer is solely on his toolkit nowadays. This is however a trap, I believe, and we definitely need to avoid to let technology ultimately drive our thinking when building a sourcing function. During my session I will share how we have embedded technology within Randstad Sourceright’s EMEA Sourcing Centre. How we made choices on when and when not to buy tech and where the human part is proved to be a still greater asset than any other tools or techs on the market.

Intuition's Fall from Grace - Algorithms and Data in (Pre)-Selection by Colin...

Textkernel

On 2 June during Textkernel's conference Intelligent Machines and the Future of Recruitment, Colin Lee presented his work on the automated preselection of applicants. For this research he used data from Connexys from 441,768 applicants at 48 companies, in combination with Textkernel parsing and normalization, to develop an algorithm that predicts which applicants get invited to a job interview. Colin explains the logic behind his approach and discusses potential future applications.

Uw database als waardevolle sourcing tool

Textkernel

Maak van je kandidaten-database je meest waardevolle sourcing tool Je kandidaten-database is een waardevolle sourcing tool. Beperkte zoekopties in recruitmentsystemen zorgen ervoor dat de database niet optimaal gebruikt wordt. Ontdek hoe je de waarde van je recruitmentsysteem maximaal kunt benutten. Aan de hand van praktijkcases laat Gerard Mulder, CCO bij Textkernel, zien hoe semantische technologie je bestaande database kan omzetten in een efficiënte sourcing tool, door: - meer sollicitaties met een gebruiksvriendelijk sollicitatieproces - krachtige semantische zoeksoftware - automatische aanbevelingen van matchende kandidaten op je vacatures. Over Gerard Mulder Als commercieel directeur sinds 2005 heeft Gerard Mulder Textkernel helpen opbouwen tot een succesvolle internationale onderneming. Gerard heeft passie voor recruitment-innovatie en technologie. Hij begrijpt de behoeften in de veranderende markt en samen met het team creëren ze technologie voor de toekomst van global recruiting.

Innovatie en de Candidate Experience (Textkernel) - Recruitment Innovation Event

Textkernel

Jobfeed rapport: De Nederlandse online arbeidsmarkt in Q1 2015

Textkernel

Het aantal vacatures in het eerste kwartaal van 2015 is met 19% gestegen. Dat blijkt uit cijfers van Jobfeed, de Big Data tool voor vacatures van Textkernel, die alle online vacatures geplaatst in Q1 2015 heeft verzameld, ontdubbeld en gecategoriseerd. In dit rapport vindt u de analyse van de vacaturedata in het eerst kwartaal van 2015. Het rapport bevat cijfers over vacaturedata, vacatures per beroepsklasse, branche, opleidingsniveau en provincie. Voor meer informatie, bezoek www.jobfeed.nl.

Etat des lieux de l'offre d'emploi en ligne - Q1 2015

Textkernel

Presentatie Jobfeed België

Textkernel

Op donderdagavond 5 maart vond in Gent de officiële en exclusieve voorstelling van Jobfeed België plaats. Jobfeed, de toonaangevende Big Data tool voor vacatures is nu, na Nederland, Duitsland en Frankrijk ook beschikbaar in België. In 2003 startte Textkernel met het samenvoegen van vacature-informatie voor matching- en analysedoeleinden onder het label “Jobfeed”. Inmiddels is Textkernel marktleider in dit domein in Nederland en is Jobfeed gaan uitbreiden naar andere Europese landen zoals Duitsland, Frankrijk en nu ook in België. De voorstelling van Jobfeed België werd georganiseerd door HRLinkIT en Textkernel. http://hrlinkit.be/ http://www.textkernel.nl/

Webinar: Vacatures in Nederland (Jobfeed & Jacco Valkenburg)

Textkernel

Deze presentatie is gegeven tijdens het webinar: Vacatures in Nederland door Kim Pieschel (Jobfeed/Textkernel) en Jacco Valkenburg (Recruit2). Met de statistieken van Jobfeed en de kennis van Jacco worden inzichten gegeven in de Nederlandse vacaturemarkt. + Wat zijn de grootste beroepsklassen en branches + Welke organisaties hebben de meeste vacatures + Wat zijn de grootste vacaturesites + Hoe kiezen recruiters vacaturesites + Waar komen sollicitanten vandaan + Wat zijn succesvolle wervingskanalen Voor meer informatie, neem contact op Kim Pieschel: pieschel@textkernel.nl, http://www.jobfeed.nl/home.php Jacco Valkenburg, jacco@recruit2.com http://www.recruitingroundtable.nl/

More from Textkernel (20)

Textkernel Emerce eRecruitment - 6 april 2017

Practical Deep Learning for NLP

AI Reality: Where are we now? Data for Good? - Bill Boorman

Robots Will Steal Your Job but That's OK - Federico Pistono

Semantic Interoperability in the Labour Market - Martin le Vrang, Team leader...

Pablo de Pedraza: Labor market matching, economic cycle and online vacancies

New Developments in Machine Learning - Prof. Dr. Max Welling

The Agile Future of HR and Talent Acquisition - Prof. Dr. Armin Trost

Human > Machine Interface - The future of HR | Perry Timms, Founder & Directo...

Ton Sluiter: Breaking Barriers and Leveraging Data

How semantic search changes recruitment - Glen Cathey

The Role of Public Innovation and the Impact of Technology on Employment - Re...

It’s all about Technology... oh wait! It’s not - Balazs Paroczay

Intuition's Fall from Grace - Algorithms and Data in (Pre)-Selection by Colin...

Uw database als waardevolle sourcing tool

Innovatie en de Candidate Experience (Textkernel) - Recruitment Innovation Event

Jobfeed rapport: De Nederlandse online arbeidsmarkt in Q1 2015

Etat des lieux de l'offre d'emploi en ligne - Q1 2015

Presentatie Jobfeed België

Webinar: Vacatures in Nederland (Jobfeed & Jacco Valkenburg)

Recently uploaded

FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf

FIDO Alliance

FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf

FIDO Alliance

Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024

Tobias Schneck

As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other? Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.

From Siloed Products to Connected Ecosystem: Building a Sustainable and Scala...

Product School

FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf

FIDO Alliance

Transcript: Selling digital books in 2024: Insights from industry leaders - T...

BookNet Canada

The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more. Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/ Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.

To Graph or Not to Graph Knowledge Graph Architectures and LLMs

Paul Groth

Knowledge engineering: from people to machines and back

Elena Simperl

Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...

Thierry Lestable

Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...

Jeffrey Haguewood

Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows. We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases. This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams. Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.

GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...

James Anderson

Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management. The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM). Speakers: Bob Boule Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle. Gopinath Rebala Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.

Key Trends Shaping the Future of Infrastructure.pdf

Cheryl Hung

Monitoring Java Application Security with JDK Tools and JFR Events

Ana-Maria Mihalceanu

GraphRAG is All You need? LLM & Knowledge Graph

Guy Korland

Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs. 1. Unifying Large Language Models and Knowledge Graphs: A Roadmap. https://arxiv.org/abs/2306.08302 2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs: https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/

Securing your Kubernetes cluster_ a step-by-step guide to success !

KatiaHIMEUR1

Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster. However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks. In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.

Encryption in Microsoft 365 - ExpertsLive Netherlands 2024

Albert Hoitingh

Bits & Pixels using AI for Good.........

Alison B. Lowndes

From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...

Product School

Elevating Tactical DDD Patterns Through Object Calisthenics

Dorra BARTAGUIZ

After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!

Leading Change strategies and insights for effective change management pdf 1.pdf

OnBoard

Recently uploaded (20)

FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf

FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf

Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024

From Siloed Products to Connected Ecosystem: Building a Sustainable and Scala...

FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf

Transcript: Selling digital books in 2024: Insights from industry leaders - T...

To Graph or Not to Graph Knowledge Graph Architectures and LLMs

Knowledge engineering: from people to machines and back

Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...

Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...

GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...

Key Trends Shaping the Future of Infrastructure.pdf

Monitoring Java Application Security with JDK Tools and JFR Events

GraphRAG is All You need? LLM & Knowledge Graph

Securing your Kubernetes cluster_ a step-by-step guide to success !

Encryption in Microsoft 365 - ExpertsLive Netherlands 2024

Bits & Pixels using AI for Good.........

From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...

Elevating Tactical DDD Patterns Through Object Calisthenics

Leading Change strategies and insights for effective change management pdf 1.pdf

Textkernel talks - introduction to Textkernel

2. About Textkernel • Founded in 2001, currently 65 employees • R&D spin-off from research in Natural Language Processing and Machine Learning at the Universities of Tilburg, Antwerp and Amsterdam • Specialised in information extraction, web mining and semantic searching & matching in the HR industry

3. About Textkernel Talks • Technical and practical presentations from research and industry specialists • Topics: NLP, IR, deep learning, semantic search, LTR, ... • Previously: (semi-)private events, irregular frequency • Plan: public monthly events (our meetups + hosting other meetup groups) • Ideas? → talks@textkernel.nl

4. About me • Software Engineer at Textkernel since January 2012. • Working on core products and on new prototypes / innovation ideas • MSc in Computer Science @ FTN, Novi Sad, Serbia • BSc (hons) in Software Engineering @ RAF, Belgrade, Serbia • hackathons, craft beer, digital photography and black metal • Find me at <almostAnySocialNetwork>/dachaz

5. • A small overview of Textkernel’s products • Boaz Leskes talks about Resiliency in Elasticsearch Today

6. • A small overview of Textkernel’s products • Boaz Leskes talks about Resiliency in Elasticsearch • pizza and beer! (and sodas) Today

7. Harvester Extract! Sourcebox Match! JobfeedSearch! Empowering recruitment

8. Textkernel products in a recruitment process

9. Automatically converts resumes into complete candidate records Extract! CV Parsing Sourcebox Document workflow, automating the bulk of application processes

10.

11. • A positive candidate experience (quicker application process) • Time saving when entering resumes • Structured data allowing better search Extract! & Sourcebox

12. Easily, effectively and efficiently find candidates in multiple databases and social media powered by Elasticsearch Search!

13. Auto suggest Search with synonyms Semantic search

14. Searching in multiple sources

15. • User-friendly interface • Simultaneously searching multiple databases and social media • Save external profiles quickly and easily Search!

16. Automatically get an overview of relevant candidates for a given job and vice versa Match!

17. Upload a job posting and automatically get a search query Search with a job ad

18. • Automatic, fast and relevant • Transparent search • No tedious and expensive query formulation • Fast and cost-effective way to fill positions Match!

19. Search and analyse real-time online job ads as well as historical data powered by Elasticsearch Jobfeed

20. Jobfeed Search all online jobs by criteria like organisation, intermediary and sector

21. A job from Jobfeed is converted into a query and matched to the candidate database Match integration

22. Automatic collecting and publishing of job ads from customer’s websites onto job boards Harvester

23. Why did you tell me all of this? • Giving you a (vague) idea about our products and technologies → hint what you can expect on future meetups

24. Why did you tell me all of this? • Giving you a (vague) idea about our products and technologies → hint what you can expect on future meetups • We’re hiring! → http://www.textkernel.com/jobs/

Textkernel talks - introduction to Textkernel

Recommended

Recommended

More Related Content

What's hot

What's hot (15)

Similar to Textkernel talks - introduction to Textkernel

Similar to Textkernel talks - introduction to Textkernel (20)

More from Textkernel

More from Textkernel (20)

Recently uploaded

Recently uploaded (20)

Textkernel talks - introduction to Textkernel