Natural Language Processing Makes the Biggest Difference Across These 4 HR Functions

The growing need for intelligent systems to streamline HR functions has pushed Natural Language Processing (NLP) into the spotlight in recent years. 

At its core, Natural Language Processing enables machines to interpret human language. In HR, NLP translates to automated chat assistants for answering employee queries, automatic translation tools for democratized training, and sentiment analysis for predicting attrition rates and sharing feedback.

NLP undeniably holds promise for HR professionals and their teams; however, like any technology, it carries potential risks, and its efficacy hinges on the humans who operate it.

This article highlights the top 4 HR functions where NLP makes the most significant difference and also examines the potential risks of NLP in HR. 

Understanding the Basics of NLP in HR

Natural language processing is a subset of artificial intelligence that enables machines to comprehend, analyze, and even produce human language. In the HR domain, this means modern systems can now “interpret” job descriptions, “process” candidate responses, and “understand” employee feedback, including the sentiment, tone, and context.

NLP combines computer science, linguistics, and artificial intelligence to make spoken or written language more comprehensible to machines. The following are the key elements of natural language processing:

• Text tokenization breaks down text into smaller elements, such as words or phrases, for easier processing.

• Sentiment analysis helps assess the emotional tone within text, like gauging mood in employee feedback.

• Named Entity Recognition (NER) detects and classifies entities such as names, dates, or places within written content.

• Semantic search improves search accuracy by interpreting the meaning behind queries, beyond just matching keywords.

• Chatbots and conversational AI use natural language understanding and generation to automate HR conversations.

The Top 4 Natural Language Processing Applications in HR

The following are the four HR areas where NLP application can positively influence workplace productivity:

1. Recruitment assistance

Manual resume screening is inherently inefficient and erroneous. Some recruiters emphasize keywords and, therefore, overlook potential talent. However, NLP's search, ranking, and matching mechanisms are far more sophisticated and data-driven.

Natural language processing in recruitment can perform the following functions:

• Precisely match employers and candidates by extracting relevant skills, career histories, and experiences outlined in resumes, professional profiles, or job descriptions.

• Swiftly identify ideal candidates using a powerful semantic search engine that enables pre-selection of top talent for job openings.

• Customize job searches for candidates using advanced filters and multi-criteria search options.

• Automate data entry when sourcing new profiles or updating internal profiles.

• Mapping internal skill sets to identify high-potential employees or areas where upskilling may be needed.

2. Learning & development (L&D)

Using semantic search, natural language processing can identify employee skill gaps and career progression interests. This allows those in charge of workplace training to inform and personalize upskilling initiatives. This is particularly beneficial in the tech industry, where continuous technological disruptions make skill gaps widely common. 

In addition to this, employees with varying literacy levels or non-native speakers can improve career mobility and development because natural language processing tools can break down language barriers and deliver training in employees’ preferred language.

Modern NLP platforms can now:

• Suggest learning resources aligned with previous feedback or career progression goals.

• Support managers in sharing more precise, more actionable feedback.

• Scan written performance evaluations for recurring themes, both strengths and issues.

• Help make learning content more accessible using NLP tools like automatic translation and speech-to-text systems. 

The outcomes of these are apparent in a more growth-oriented workplace culture. 

3. Sentiment analysis

Sentiment analysis helps interpret the subjective language within text data, such as written feedback, emails, and survey results. It detects words that typically carry some emotional nuance, such as email exchanges between a manager and a direct report that might indicate anger or dissatisfaction. NLP can recognize such underlying emotions and determine whether a specific statement is positive, negative, or neutral.

Sentiment analysis enables HR teams to:

• Monitor employee morale in real-time rather than relying on annual surveys.

• Spot signals of potential workplace issues (attrition risks) early before they escalate.

• Make informed, data-backed decisions to improve engagement and morale.

For example, if there are signs of frustration or dissatisfaction in a specific internal communication, NLP can tag them to HR, who can take action and mitigate escalation. This proactive approach supports a healthier work environment and helps improve retention.

4. HR Chat Assistants 

HR professionals are expected to manage a substantial volume of employee requests every day. These may be for administrative needs (such as pay slip requests, leave balances, or expense claims) or organizational purposes (like policy clarifications or onboarding support). 

Timely access to accurate information is crucial, and HR chat assistants can help HR professionals to respond to every request or query quickly and efficiently.

Often integrated into corporate messaging platforms, it reduces the burden on administrative teams by handling recurring queries on the company, its operating policies, and internal processes.

Challenges of NLP in HR Applications

Common challenges and risks associated with NLP-based tools are discussed below:

1. Potential for dehumanizing candidates 

Removing names and photos from resumes may help reduce bias (such as racial or gender discrimination). However, the increasing automation of hiring and HR processes through NLP risks further dehumanizing the applicant. In fact, the term “human resources” itself can be seen as reducing individuals to abstract “resources,” stripping away their identity and complexity.

Direct human interaction between potential employees remains valuable to recruitment and ongoing people management.

2. Bias prevalence

NLP-based systems may reflect biases present in job-related data. These may originate from stereotypes (e.g., assumptions about certain gender groups), indirect indicators of sensitive attributes (e.g., links between language use and race), and structural barriers (e.g., limited access to technology). Therefore, it is important to recognize and minimize the harmful impact of these biases when implementing NLP in real-world applications. 

3. Process clarity issues

Lack of process clarity is a common complaint among candidates navigating automated resume screening systems. Without a recruiter providing updates regarding the application process, candidates may feel disconnected or left in the dark.

There are ways to address this challenge. Companies can integrate a workflow-aware chatbot for applicants to query about their status (e.g., via email). It can also proactively notify them about the follow-up interview stages and the current status of their application (for instance, “The hiring manager looked at your résumé today”).

Conclusion 

While natural language processing is relatively under-leveraged in the HR domain (given the complexity of HR problems), its impact so far has been significant. NLP-fueled recruitment and feedback tools can significantly streamline HR workload and positively transform workplace culture. NLP in HR can also improve career mobility and development in industries with skill shortages. 

However, it is imperative to approach emerging technologies like NLP in HR with caution and explore ethical issues before scaling them broadly across divisions.