How Natural Language Processing can Revolutionize Human Resources

Natural language processing has a significant relevance to HR

Did you know that text analysis has been the most prevalent productivity tool over the past 3 decades or so for HR? It is very familiar to HR.

Key benefits for HR with reference to natural language processing

Benefits are many, corresponding to varying levels of engagement and investment by HR.

How do the insights from natural language processing analysis impact HR?

HR specific NLP analysis, with varying and often progressive levels of insights not only acts as decision supports (DSS). But also, enable greater accuracy and speed to key HR business processes and improving HR metrics. They also reduce human bias in decision-making application. Examples include resume scoring and survey analysis.

Misinformation with regards to adoption of natural language processing in HR Processes

It is not the case that natural language processing systems replace HR. On the contrary, these systems further empower HR personnel within their organization.

Bottlenecks in adopting natural language processing to HR

There aren’t many vendors who are only focused on advanced NLP to HR processes yet. Most vendors are text analytics generalists; they may not have in-depth aware of HR specific challenges. OrganizationView is a good example of a dedicated operator in this space and there are a few more.

Identified approaches in NLP that are relevant to HR

Operational HR should take the lead and identity relevant application areas within their own organizations. The impact of NLP in HR is likely to depend upon data availability, security, integration, company policy or any other specific business requirements.

  • Sentiment Analysis of HR documents
  • Deep Information Extraction from HR documents
  • Classification/ ranking of HR documents as per business specifications
  • Automated Summation of HR documents (topic discovery)
  • Establishing HR Hypothesis and process improvement (a part of prescriptive analytics)
  • Application/ Resume classification and scoring
  • Appraisal and 360-degree feedback analysis
  • Surveys and feedback analysis
  • Identifying Training, Succession planning
  • Social media content analysis of employees
  • Insights on documented Legal cases/ suits
  • Design and insights about Employee Counseling
  • NLP on virtually any unstructured data within the scope of HR, including transcribed data.
  • Statistical Tagging
  • Symbolic Tagging

A business case of NLP in a key HR process (Hiring)

The basic approach of natural language processing remains more or less the same across all types of unstructured data. However, for the sake of familiarity let’s take the example of resume scoring in Hiring on a large unstructured dataset

  • Classify and rank resumes according to their core skills, experience or any other priory. Like desirable skills and professional experience.
  • Classify resumes according to their format styles. Like chronological, reverse chronological, hybrid, skills-based, and qualification based functional based formats.
  • Identifying basic sections of a resume (topic model based on the priority given by HR)
  • Identify gaps in professional/ academic records in resumes
  • Identify potential fraud/ incorrect information and anomalies in resumes
  • Deep information extraction from resumes. For instance combination of professional skills/ education + university rankings + professional experiences + environment and context + international assignments/ location specific + awards/ recognitions + recommendations/ professional network ) via compound “conditional rules models”
  • Periodic and automated evaluation of dataset via batch jobs and database procedures/triggers/functions
  • Automated scoring and classification of datasets via above
  • Sending an automated email to shortlisted candidates (for example a test set or interview call) or sending consolidated or specific reports to the HR/Recruitment team.

Typical services offered by NLP vendors

NLP vendors typically offer a combination of services mentioned above, including summation, topic modeling, and conditional rules models.

The Future…

HR is the prime candidate for adoption of NLP-based technologies, as HR is inherently people-centric and communication based. HR business processes thus generate vast amounts of natural language data.

Raja Sengupta

Raja Sengupta is a Data Scientist, Statistician and Researcher on Computational Linguistics (specialized for HR). he has 17+ years of research and consulting experience in the entire spectrum of applied statistics, analytics, Six Sigma, programming and NLP. This includes experience in project management, operational research and Six Sigma in HR/Organizational psychology. He is a thinker, innovator and writer.

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