What is Natural Language Processing (NLP)?

Natural Language Processing (NLP) is defined as a branch of artificial intelligence that helps algorithms to interpret written and spoken languages such as English, Spanish, and French. NLP uses multiple disciplines such as computer science and computational linguistics to bridge the gap between human language and computer understanding.

For instance, when you ask Siri to make a restaurant reservation for you, Siri uses NLP algorithms to decipher your intent based on your command. NLP algorithms can sense the difference when you say, “book a table for two at Morton’s Steakhouse at 8 PM tomorrow,” and when you say, “book a table for two at Morton’s.” If it’s the latter, Siri will follow up with a question like, “What time do you want me to book you a table at Morton’s Steakhouse?” and when you say, “8 PM, tomorrow,” Siri will understand and correlate that this is a continuation of the last message where you asked it to make a reservation.

NLP algorithms today can analyze more language-based data than humans in a more consistent and unbiased way. Considering the complexity of languages – the dialects, the grammar and syntax rules, terms, and slang, NLP is crucial to scaling language-related tasks and often does a much better job than humans.

The Role of Natural Language Processing in HR 

As a business function that is heavily reliant on human communication, NLP can be applied to the first resume screening to the exit interview. Additionally, as HR chatbots and text analytics become ubiquitous across the HR function, NLP can help HR teams turn massive amounts of text into quantifiable insights almost instantaneously. This can be used to inform decisions about retention, engagement, the employee experience, and even employee performance. So, how can HR leverage NLP solutions to improve its efficacy and streamline processes?

Let’s look at some of the critical applications of NLP in HR:

NLP in Recruitment

As a recruitment professional, you understand how challenging it can be to sift through multiple resumes. And as a result, most recruiters tend to focus on keywords when screening resumes. This practice is in effect, one of the most inefficient and inaccurate methods to select a candidate.

NLP provides a data-driven approach to resume screening, which not only gives you more time per candidate but also helps you make better hiring decisions. NLP also enables you to rank and classify candidate profiles, identify personal traits, and eliminate human biases. It reduces time-to-hire by automating pre-qualification via chatbots and automated interviews, also improving the candidate experience.

NLP in Employee Engagement

NLP uses text analytics to provide advanced insights on employee sentiment, identifying areas of conflict, perform comprehensive feedback analysis, and survey analysis. Based on these insights you can measure and design an employee engagement strategy that addresses employee pain-points and drives engagement.

AI-powered engagement solutions can analyze hundreds and thousands of employee comments from pulse surveys to identify the most relevant keywords and phrases in your organization to quickly summarize and provide a gist of employee feedback. Moreover, collaboration and communication tools like Slack and emails can be layered with an NLP solution to provide HR teams real-time visibility into employee sentiment and morale.

NLP in Employee Social Media Analytics

HR is the prime candidate for adoption of NLP-based technologies due to its people-centric and communication-based nature. As AI solutions continue evolving, NLP will become a must-have technology for forward-thinking organizations.

NLP is an effective “listening” tool for HR teams to analyze social media content of employees to uncover areas of interest, identify employee potential and talent, identify competence, and track behavior trends. Insights based on social media analytics can help employers identify at-risk employees, high performers, gauge employee loyalty, and ultimately drive retention. NLP in social media analysis can also a powerful addition to your employee advocacy program and help you rationalize the ROI of your program.

Most HR business engagement generates high volumes of natural language, which is unstructured data. Think about areas like recruitment, employee feedback, surveys, appraisals, learning, legal cases, counseling etc. Additionally, legacy HR processes and forms can be re-engineered to accumulate ever increasing volumes of natural language data via an active policy of audio recording & transcribing or even a slight redesign of various HR processes forms/surveys/applications.

Key benefits for HR with reference to NLP

Benefits are many, corresponding to varying levels of engagement and investment by HR. It starts from generic text analytics (sentiment analysis). Goes to advanced insights (via computational linguistics models) and can even include potential semi-automation.

Application areas of NLP in HR

How do the insights from NLP 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.

1. Types of Generic natural language processing insights (relevant to the HR application context)

  • 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)

2. Application areas of natural language processing (within the HR application context)

  • 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.

3. Overview of various NLP methodologies employed by vendors (within the HR application context)

  • Statistical Tagging

Statistical tagging offers insights from various levels of granularity starting from basic text classification, sentiment analysis to deep information extraction and topic modeling/ automated summation. Some of the popular information extraction/ topic discovery approaches are Conditional Random Fields, Hidden Markov Models, and LDA.

  • Symbolic Tagging

The HR familiarity with basic Boolean keyword searches to identify good resumes is a very good example of symbolic tagging. But today NLP models like nested, iterative and conditional “regular expressions” can fine tune symbolic tag searches to the deepest possible levels of granularity.

A combination approach of statistical and symbolic tagging is often referred to as a “conditional rules model” within the NLP context. Tailored combinations of “conditional rules models” are typically developed via integrated cohort analysis in collaboration with HR.

  • Symbolic Tagging

The HR familiarity with basic Boolean keyword searches to identify good resumes is a very good example of symbolic tagging. But today NLP models like nested, iterative and conditional “regular expressions” can fine tune symbolic tag searches to the deepest possible levels of granularity.

A combination approach of statistical and symbolic tagging is often referred to as a “conditional rules model” within the NLP context. Tailored combinations of “conditional rules models” are typically developed via integrated cohort analysis in collaboration with HR.

 Key use cases of AI in the HR Context

  • Image and video recognition: Deep learning algorithms outperform humans in object classification. Given videos and photos of thousands of applicants, deep learning systems can identify and classify candidates based on objective data.
     
  • Speech recognition: While understanding human voice and myriad accents is difficult for most machines, deep learning algorithms can be designed to recognize and respond to human voice inputs. Virtual assistants use speech recognition algorithms to process human voice and respond accordingly.
     
  • Chatbots: Natural language processing (NLP) trains chatbots and similar systems to understand human language, tone, and context. NLP will emerge as a crucial capability for AI systems as organizations continue to automate HR service delivery with chatbots.
     
  • Recommendation engines: Digital learning experiences often involve personalized learning recommendations related to skill levels and professional interests. Using Big Data and Deep Learning, learning experience platforms can identify learning pathways that might interest individual employees.