How to transform HR via computational linguistics. A presentation written and designed by Hendrik Feddersen and Raja Sengupta

1. TEXT ANALYTICS APPLIED TO HR
How to transform HR
via computational linguistics
Presentation written and designed by Hendrik Feddersen and Raja Sengupta
Frequency : TF and IDF

2. xv
"How can computational linguistics
help business create value, drive
action and impact without the hassle
of going through intensive HR
reporting?
Presentation written and designed by Hendrik Feddersen and Raja Sengupta

3. Hendrik Feddersen
Head of HRIS
European Medicines Agency
My journey so far
• SAP HCM implementation, problem solving,
data cleaning, reporting, predictions and
training colleagues
• Chairman to more than 100 open selection
procedures of about 150 candidates each
• Continuous business improvements
(Lean Six Sigma Green Belt certified)
• Self-learning “R language” and Data Science
• Passion for HR Analytics, writing posts on
LinkedIn, tweets and connecting
internationally

4. Building
the case
for Text
Analytics
in HR
Text or natural language is omnipresent. Human
communication inherently involves languages, even
our deepest internal thoughts can be documented
via natural language. This talk itself could be
transcribed and text processed for meaningful
insights, the magnitude and scope of text analytics
application is limitless.
This enables NLP analysis with unique, unparalleled
insights not limited to business alone but to the
human intent and disposition itself, a key for HR.

5. The History and Challenges
of Text Analytics
LATE STARTER
Natural Language originates with genesis of
Human civilization itself, yet text
analytics has been a relatively slow-
starter.
RESOURCE INTENSIVE
Inherent variation and volumetric content,
makes analytics on human language an
extremely resource intensive process.
SEMANTIC DISTRIBUTION
The mathematic challenge lies in mapping
stochastic characteristics to adequately
deterministic, based on the ZIPF’s law.

6. Text Analytics Today
THRUST OF INFORMATION TECHNOLOGY
The thrust of information technology in the
early part of this century has helped
immensely the cause of text analytics.
R&D IN STATISTICAL TAGGING OF TEXT
Taking advantage of improved computing
capabilities, vast repositories of libraries
dedicated to statistical text modeling text
were compiled, nosql type databases
developed and extensive R&D in text
analytics has been carried out.
SEMANTIC CONTENT OF HR DATA
Most HR processes are textual or
unstructured, making thereby HR a key
beneficiary of NLP research.

7. Text Data generated by HR
TEXT DATA FROM CORE HR PROCESSES
Most core HR operations including application
evaluation, selection process, appraisal
form and management, 360 assessments,
staff engagement surveys, employee
feedbacks, etc., all generate a wealth of
semantic data.
OTHER TEXT DATA AVAILABLE TO HR
In addition the professional and personnel
social networking records like LinkedIn,
Facebook, Twitter provide HR implicit
access to potential capabilities and
behavioural trends of individuals.
TRANSCRIBED TEXT / VOICE TO TEXT
Ongoing research of key relevance for HR.

8. Applications of Text
Analytics to HR process
TEXT ANALYTICS MODELS ON HR
PROCESSES DATA HELP IMPROVE
• Applicant hiring & employee monitoring
• Employee appraisals and feedback
management
• Employee welfare initiatives and
complaint management systems
• Creative HR based Insights via text
based surveys
• Early warnings and insights on potential
behavioural trends and legal issues.

9. Skepticism of operational HR
towards Text Analytics
• Traditional HRIS reporting systems suffice
• Issues of integration with existing HRIS
systems
• Data protection and compliance
• Learning, training and support
• New technology skepticism
• Complexity and potential failure
• Potential job loss via automated text scoring
models
• Challenges of Multi lingual text environment
• Inherent limitations of machines in
understanding delicate nuances of human
communication and behaviour

10. Countering Skepticism of
operational HR
Traditional HRIS reporting does not cater to
predictive and prescriptive modeling
Modern HR Analytics systems seamlessly
integrate with existing legacy HRIS systems
Step wise evidence based production
implementation
HR Analytics cater for all existing European
standard data protection and compliance
Full integrated video based training and support
along with implementation
Text scoring models are a capable decision
improvement system, however they are not a
replacement for human expertise
POS tagging in German, French and Italian
seamlessly cater for Multi lingual text analytics

11. What is Text Analytics?
Brief Technical Overview
Natural Language Processing
NLP involves mathematical operations on
linguistic units, i.e. words, expressions,
sentences, paragraphs in various
permutations and combinations in order to
derive meaningful insights via their statistical
tagging.
Statistical Tagging
Statistical tags include vectoring, tokenizing,
filtering, stemming, lemmatization, n-grams
and topicalisation, etc.
Objective
The objective is for machines to decipher the
thought and intent vector behind human
language via test of statistical significance of
statistical tags and provide us with insights
relevant to our interests.

12. Basic Text Analytics routines
relevant to HR process data
Supervised
Naive Bayes, SVM, Neural Networks,
Decision Trees, Various ensembles
Unsupervised
Principle component analysis,
Clustering via distance matrix’s
Multivariate Semantic Outliers,
Association Rules & FP-Growth
Semi-Supervised
Conditional Rules Based Dictionary Taggers

13. Hidden Markov Models
HMM can be considered to be an
implementation of Bayesian joint
probability distribution in text analytics, i.e.
mapping the unobservable based on
probabilistic ranking of the observable
term, hence the use of the term hidden
Latent Dirichlet Allocation
LDA is a adoption of SVM, essentially a vector
based decomposition method typically
using a cosine distance matrix between
words in the document. This is supposed
to unravel the synonymy and polysemous
relationships between words via statistical
inference and help discover topics.
Basic Text Analytics routines
relevant to HR process data

14. Live
example
of Text
Analytics
in HR
Automated Resume Scoring –
A real life HR Analytics example
Business Problem
A. Recruiters typically estimate job applicant fitment via their
specific process expertise,
B. This is often a tedious, manual time and consuming process,
C. Quality of fitment estimations can be inconsistent and
subject to bias.
Solution
Statistical text classification models, can improve upon
A. Absolute accuracy of application fitment estimations,
B. Overall consistency of correct application estimations,
C. Improve the skills of recruiter, and assist in fine tuning
searches,
D. Greatly reduce time and effort.

15. Automated Resume Scoring –
A real life HR Analytics example
Success Parameters of the Model:
A. At least 80% of the accuracy of manual forecasting,
taking a fraction of the time required,
B. In addition to the above providing statistical visualization and insights
via multivariate maps helping improve technical skills of the recruiter.
Challenges (of this specific business case):
A. Inadequacy of learner base (scored data) for the supervised
classification model,
B. A scattered data-set that required significant pre-processing in order
to unify for analytics,
C. Significant variation among the different job orders.

16. Automated Resume Scoring –
A real life HR Analytics example
Methodology Employed - Technology
The severe inadequacy of the learner base meant that typically used supervised
classification models could not be employed. More than 15 text based
information modules are scored for each candidate In addition to learner base
inadequacy cross validation of the models would not have been reliable.
Iterative semi-supervised conditional rules dictionary had to be built Once built
the model is reusable for job orders with same (or similar) specifications. Our
model can typically score 500 applications in less than 5 minutes!
The estimated accuracy is 80%, corroborated via HR domain expert.
The following step wise approach was used for building the model.

17. Step 1: Structured Query Language
Purpose : Consolidated text (string) data
stored across multiple tables
Output : Single unified XLS report

18. Step 2: Near codeless (some java script) based ETL
Data: Multiple XLS reports
Purpose : ETL - Extract, Transform and Load
to preprocess data for text analysis
Output : Single unified XLS report

19. Step 3: Co-occurrence network of maps [illustration only]
Part-of-speech tagging : Noun, proper nouns, bi and tri gram tags
Distance Metric : Jaccard’s similarity coefficient
Frequency : Term frequency–inverse document frequency

20. Step 4: Self organising maps
POS tagging: Noun, proper nouns, bi and tri gram tags
Distance Metric : Euclidean distance
Frequency : TF and IDF

21. Step 5: Kruskal Nonmetric Multidimensional analysis
POS tagging: Noun, proper nouns, bi and tri gram tags
Distance Metric : Mahalobnis distance
Frequency : TF and IDF

22. Step 6: Kruskal Non-metric Multi Dimensional Scaling
POS tagging: Noun, proper nouns, bi and tri gram tags
Distance Metric : Jaccard coefficient
Frequency : TF and IDF

23. Step 7: Latent Dirichlet allocation (LDA) Subtopic Discovery
POS tagging: Noun, proper nouns, bi and tri gram tags
Method : Parallel LDA
Frequency : TF and IDF

24. Step 8: Hierarchical Cluster Analysis
POS tagging: Noun, proper nouns, bi and tri gram tags
Method : Ward and Mahalanobis distance
Frequency : TF and IDF

25. Step 9: Conditional Rules Dictionary via python wrappers
Data: Computational Linguistics from multidimensional maps
Purpose : Creating coding dictionary to score application forms
Output : Single unified XLS report of scored applications
[for illustration only]

26. Step 10 OUTPUT: Single unified XLS report of scored
applications arranging in descending order

27. Further info
Raja Sengupta provided technical support for this
presentation.
Raja is a Computational Linguistics Professional with
research background in applying text analytics to
operational HR.
One of the many solutions he is working on is
KNOHR: A scoring system for job applicants,
appraisals, text surveys, which can be adopted for
semantic data analysis, while processing operational
HR data.
The system based on python natural language tool kit,
integrates seamless with existing HRIS systems
provides efficient decision support reports via a single
click or periodically via batch process with over 80%
cross validation accuracy.
Raja Sengupta
HR Analytics Developer

28. Summary
The intent here was to provide an overview of the body
of work around natural language processing, including
its relevance and application for HR Analytics.
The predominantly semantic nature of data generated
by HR processes was emphasised, making the case for
strong relevance of text analytics in the HR domain.
A live production example was demonstrated to
emphasise the real time constraints and creative semi
supervised approaches required, often under
supervision of operational HR, in order to achieve an
acceptable level of model accuracy.
This is the very reason, why text analytics models shall
continue to evolve as a very efficient decision support
system, however it cannot replace humans expertise
and capabilities in understanding the nuances of
human language.
Text
Analytics
in HR