A Glossary of Key Terms in Natural Language Processing (NLP) & Semantic Understanding

In the evolving landscape of talent acquisition and HR, understanding the core concepts behind artificial intelligence and automation isn’t just an advantage—it’s a necessity. Natural Language Processing (NLP) and semantic understanding are at the heart of many transformative technologies, from AI-powered recruitment platforms to sophisticated applicant tracking systems. This glossary provides HR and recruiting professionals with clear, actionable definitions of key terms, highlighting how these technologies can be leveraged to streamline operations, enhance decision-making, and secure top talent. Dive in to demystify the language of AI that is reshaping our industry.

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of artificial intelligence that empowers computers to understand, interpret, and generate human language in a valuable way. For HR and recruiting, NLP is the engine behind intelligent resume parsing, allowing systems to extract key information like skills, experience, and qualifications from unstructured text. It automates the initial screening process, sifting through hundreds of applications to identify the most relevant candidates based on job descriptions. Beyond just matching keywords, advanced NLP can understand the nuances of language, significantly reducing manual review time and ensuring a more efficient, objective candidate evaluation process, ultimately saving high-value employees from low-value work.

Semantic Understanding

Semantic understanding goes beyond simply recognizing words; it focuses on comprehending the meaning, context, and intent behind human language. While NLP handles the ‘what,’ semantic understanding tackles the ‘why’ and ‘how.’ In HR, this means an AI system can interpret a candidate’s resume not just for keyword matches, but for the actual meaning of their experience relative to a role’s requirements. For example, it can understand that “managed a team of 10” implies leadership skills, even if the word “leader” isn’t explicitly used. This deeper level of comprehension allows for more accurate candidate matching, personalized outreach, and better prediction of cultural fit, moving beyond superficial keyword searches to true talent alignment.

Tokenization

Tokenization is one of the foundational steps in NLP, where a block of text is broken down into smaller units called “tokens.” These tokens can be words, phrases, symbols, or even individual characters, depending on the specific NLP task. For HR professionals, tokenization is crucial in the initial processing of resumes, job descriptions, and candidate communications. Before any AI can analyze text for meaning or sentiment, it must first be segmented into these manageable units. This process enables systems to count word frequencies, identify key phrases, and prepare the data for further, more complex linguistic analysis, making unstructured text accessible and workable for automated talent acquisition tools.

Named Entity Recognition (NER)

Named Entity Recognition (NER) is an NLP technique that identifies and classifies key information, or “named entities,” within text into predefined categories such as names of people, organizations, locations, dates, and specific skills. In the context of HR and recruiting, NER is incredibly powerful for automating data extraction from resumes, cover letters, and professional profiles. It can automatically pull a candidate’s previous employers, job titles, educational institutions, and specific technical skills with high accuracy. This dramatically reduces the manual data entry required to populate applicant tracking systems (ATS) or CRM platforms, speeding up candidate processing and ensuring critical information is captured consistently, freeing up recruiters for more strategic engagement.

Sentiment Analysis

Sentiment analysis, also known as opinion mining, is an NLP technique that determines the emotional tone conveyed in text—whether it’s positive, negative, or neutral. For HR and recruiting, sentiment analysis offers valuable insights into candidate feedback, employee survey responses, and even public perception of employer branding. It can gauge a candidate’s experience with the application process by analyzing their follow-up emails, or assess employee morale from internal communications. By automating the analysis of large volumes of qualitative data, organizations can quickly identify areas for improvement in candidate experience or employee engagement, leading to more responsive HR strategies and a stronger workplace culture.

Text Classification

Text classification is a machine learning technique used to assign categories or labels to text documents. In HR, this is incredibly useful for automating the organization and prioritization of various types of textual content. For instance, text classification can automatically sort incoming job applications by relevance to specific roles, flag resumes that meet specific minimum qualifications, or categorize internal HR inquiries by topic (e.g., benefits, payroll, leave requests). This automation streamlines workflows, ensures urgent matters are addressed promptly, and helps recruiters quickly focus on the most promising candidates, transforming an otherwise chaotic inbox into an organized, actionable system.

Machine Learning (ML)

Machine Learning (ML) is a subfield of AI that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. Unlike traditional programming, where rules are explicitly coded, ML algorithms “learn” from examples. For HR and recruiting, ML powers many predictive analytics applications: it can predict candidate success rates based on historical data, identify flight risks among current employees, or optimize job advertisement spending by learning which platforms yield the best hires. By continuously improving its models as more data becomes available, ML helps organizations make smarter, data-driven decisions, leading to more efficient talent pipelines and stronger retention strategies.

Deep Learning

Deep Learning is a specialized subfield of Machine Learning that uses artificial neural networks with multiple layers (hence “deep”) to learn complex patterns from vast amounts of data. It excels in tasks like image recognition, speech recognition, and advanced natural language processing. In HR, deep learning enables highly sophisticated AI applications, such as understanding subtle nuances in candidate language, recognizing emotions in video interviews, or predicting complex relationships between skills and job performance. While demanding significant computational resources and data, deep learning pushes the boundaries of AI capabilities, allowing for more granular insights and highly intelligent automation in talent management and employee experience.

Large Language Model (LLM)

A Large Language Model (LLM) is a type of deep learning model trained on massive datasets of text and code, enabling it to understand, generate, and process human language with remarkable fluency and coherence. For HR and recruiting, LLMs are game-changers, powering tools that can draft personalized job descriptions, generate engaging candidate outreach emails, summarize lengthy resumes into concise profiles, or even simulate interview responses. They can translate complex candidate data into easily digestible insights for hiring managers. LLMs enhance communication efficiency and quality across the entire talent lifecycle, automating routine content creation and allowing HR professionals to focus on strategic human interaction.

Generative AI

Generative AI refers to AI models capable of creating new content—be it text, images, audio, or code—that is novel, realistic, and often indistinguishable from human-created content. Large Language Models are a prime example of generative AI for text. In HR and recruiting, generative AI revolutionizes content creation by automating the drafting of first-pass job descriptions, crafting compelling candidate messages, or even developing custom interview questions tailored to specific roles. It significantly reduces the manual effort in creating high-quality communications and marketing materials, accelerating the hiring process and allowing HR teams to scale their outreach and branding efforts with unprecedented efficiency.

Contextual AI

Contextual AI refers to artificial intelligence systems that can understand and utilize the surrounding context in which information is presented to provide more relevant and accurate responses or actions. Unlike older AI systems that might rely solely on keywords, contextual AI considers the broader meaning, user intent, and historical interactions. In HR, this means an AI chatbot can not only answer basic FAQs but also understand follow-up questions, reference previous conversations, and adapt its responses based on a candidate’s profile or an employee’s query history. This leads to more intelligent, personalized, and helpful interactions, significantly improving the candidate and employee experience by reducing friction and providing precise information.

Vector Embeddings

Vector embeddings are numerical representations of words, phrases, or entire documents in a multi-dimensional space, where semantically similar items are located closer together. This allows AI systems to understand the relationships and similarities between pieces of text. For HR, vector embeddings enable highly sophisticated candidate matching: instead of just keyword searches, an AI can compare the “vector” of a job description against the “vectors” of thousands of resumes to find candidates whose experience is conceptually similar, even if different terminology is used. This dramatically improves the accuracy of candidate recommendations, uncovers hidden talent, and ensures a deeper, more meaningful alignment between roles and applicants.

RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) is an AI technique that combines the generative capabilities of Large Language Models (LLMs) with the ability to retrieve information from external knowledge bases. Instead of relying solely on its pre-trained data, an RAG system first searches a specific, trusted dataset (like your company’s policy documents or a curated database of HR best practices) and then uses that retrieved information to generate a more accurate, relevant, and factual response. For HR, RAG is invaluable for creating highly informed chatbots that can answer specific policy questions, generating accurate job descriptions based on internal role definitions, or providing candidates with precise information from your career site, ensuring accuracy and reducing “hallucinations” often associated with pure LLMs.

Bias Detection

Bias detection in AI refers to the process of identifying and mitigating unfair prejudices or assumptions within algorithms, datasets, or their outputs. In HR and recruiting, this is critical for promoting fairness and diversity. AI systems, if trained on biased historical hiring data, can inadvertently perpetuate or amplify those biases, leading to discriminatory outcomes in candidate screening, resume ranking, or interview scheduling. Bias detection tools analyze data and algorithms to uncover these patterns, helping organizations ensure their AI-powered hiring tools are equitable. Implementing such checks is essential for maintaining ethical AI practices, fostering an inclusive workplace, and avoiding legal repercussions, aligning technology with human values.

Chatbot/Conversational AI

A Chatbot or Conversational AI is a computer program designed to simulate human conversation through text or voice. Powered by NLP and sometimes LLMs, these tools can interact with users in a natural language format. In HR and recruiting, chatbots are transformative, providing 24/7 support to candidates and employees. They can answer frequently asked questions about job openings, application status, company culture, or HR policies. This frees up HR staff from repetitive inquiries, allowing them to focus on more complex, strategic tasks. Conversational AI significantly enhances the candidate experience by providing instant responses and automates initial screening, streamlining communication and improving operational efficiency.

If you would like to read more, we recommend this article: The Future of Talent Acquisition: A Human-Centric AI Approach for Strategic Growth