Natural-language processing (NLP)

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What is natural-language processing (NLP)?

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that enables machines to understand and interpret human language. It has gained popularity in recent years with the increased use of voice recognition devices like Google Assistant and Siri. NLP is a complex field that combines computational linguistics, machine learning, deep learning, and statistics to help machines understand the meaning, intent and emotions behind human language.

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Image showing the meaning of natural language processing in HR

What is the function of natural language processing in the field of human resources?

The function of Natural Language Processing (NLP) in Human Resources (HR) is to automate and streamline various HR processes, such as recruiting, onboarding, and employee engagement.

One way NLP can be used in HR is through automating the recruitment process by analyzing resumes and job postings to match candidates with job requirements, and identifying the most qualified candidates. It can also be used to conduct initial candidate screenings, such as phone or video interviews, by analyzing spoken and written responses to assess language proficiency and fit with the job. HBR’s AI and NLP in HR research

Another way NLP can be used in HR is to analyze employee engagement by processing feedback and survey responses, recognizing patterns and identifying areas for improvement. NLP can also be used for onboarding and training by providing new employees with personalized training materials based on their individual needs and skills.

NLP can also be used to analyze employee communications, such as email and chat, to identify potential issues, such as bullying or discrimination, and to monitor the overall tone and sentiment of the workplace.

In summary, NLP can be used in HR to automate and streamline various HR processes, such as recruiting, onboarding, and employee engagement.

What is the importance of natural-language processing (NLP)?

Natural Language Processing (NLP) is important for a number of reasons: SHRM’s AI and NLP guidance

  1. Improved communication: NLP allows for more natural and efficient communication between humans and machines, enabling better understanding and interpretation of human language.
  2. Increased automation: NLP can automate tasks such as language translation, sentiment analysis, text summarization, and question answering, increasing efficiency and reducing human error.
  3. Enhanced customer experience: NLP can be used to improve customer service by providing more accurate and personalized responses to customer inquiries, and it can also be used to analyze customer feedback to identify areas for improvement.
  4. Improved decision-making: NLP can be used to extract insights from large amounts of unstructured data, such as text and speech, to inform business decisions.
  5. Advancements in AI: NLP is a key component of AI, and the development of NLP is helping to drive the advancement of AI as a whole.
  6. Enrichment of HR processes: NLP can be used to automate and optimize various HR processes such as recruitment, employee engagement, onboarding, training and many more.
  7. Advancement in natural Language understanding: NLP is a key component of natural language understanding (NLU) and it enables machines to understand and interpret human language.
  8. Advancement in Virtual Assistants: NLP enables virtual assistants to understand and respond to human language, making them more useful and efficient.

NLP-powered HR tools enable faster, more consistent talent decisions : but require bias monitoring to ensure fair, skills-based outcomes. Organizations using pre-employment assessments ensure every hire is grounded in verified skills. A data-driven hiring plan reduces mis-hire risk, while strong talent acquisition practices focused on skills-based hiring help organizations attract and retain top talent.

Frequently asked questions

Natural language processing (NLP) is a branch of artificial intelligence that enables computers to understand, interpret, and generate human language. In HR, NLP powers applications that analyze text : resumes, job descriptions, engagement survey responses, interview transcripts, and employee communications : to extract insights, automate tasks, and improve talent decisions at scale.

Talent acquisition NLP applications: resume parsing (extracting structured skills, experience, and education data from unstructured resume text), job description optimization (analyzing language to predict which wording attracts diverse applicants), candidate-job matching (comparing resume content to job requirement text for fit scoring), chatbot-based candidate screening (conversational AI that asks pre-screening questions), and sentiment analysis of candidate feedback to improve candidate experience.

HR analytics NLP applications: automated thematic analysis of open-ended engagement survey responses (categorizing thousands of comments by theme without manual reading), sentiment analysis of employee communications (identifying rising frustration or disengagement signals), exit interview analysis at scale, analysis of performance review language for bias patterns, and HR help desk automation (chatbots answering employee HR questions in natural language).

Key risks: NLP models trained on historical data can encode and amplify past biases (Amazon’s 2018 recruiting AI downgraded resumes mentioning women’s colleges), language patterns associated with protected characteristics (writing style can correlate with race, gender, or age), lack of explainability (HR may not be able to explain why an NLP tool ranked a candidate lower), EEOC adverse impact risk, and data privacy issues from processing employee communications. Rigorous bias testing and human oversight are essential.

Vendor due diligence: request bias audit reports for any NLP tool used in employment decisions (tested for disparate impact on protected groups), require explainability documentation (can the vendor explain what features drove a specific output?), ensure legal compliance review (EEOC 2023 guidance on AI tools applies), conduct your own adverse impact analysis on outcomes from the tool, include audit rights in vendor contracts, and maintain human review of any AI-assisted employment decision.

Lower-risk NLP applications: survey theme analysis (informing programs, not individual decisions), job description language optimization, HR FAQ chatbots, and internal document search. Higher-risk applications requiring stronger governance: resume screening, candidate scoring, performance analysis, and any NLP tool that produces individual-level scores used in employment decisions. The risk level corresponds to the degree to which the output directly affects individual employment outcomes.