A Glossary of Key Terms in Artificial Intelligence (AI) and Machine Learning (ML) in Document Processing

In today’s fast-paced HR and recruiting landscape, leveraging technology is no longer an option but a necessity. Artificial Intelligence (AI) and Machine Learning (ML) are transforming how organizations handle vast amounts of documentation, from resumes and applications to contracts and compliance forms. Understanding the core concepts behind these technologies empowers HR and recruiting professionals to identify opportunities for automation, streamline workflows, and make more informed decisions. This glossary defines key terms, illustrating their practical application in optimizing document-centric processes within your hiring and operational strategies.

Artificial Intelligence (AI)

Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think, learn, and solve problems like humans. In the context of document processing for HR, AI enables systems to perform tasks such as understanding resume content, extracting key information from contracts, or even generating tailored responses in candidate communications. For recruiting, AI-powered tools can analyze unstructured data from applications, identifying relevant skills and experience far more efficiently than manual review, helping recruiters prioritize top talent and reduce time-to-hire.

Machine Learning (ML)

Machine Learning is a subset of AI that allows systems to learn from data, identify patterns, and make decisions with minimal human intervention. Instead of being explicitly programmed for every task, ML algorithms improve their performance over time as they are exposed to more data. In document processing, ML models are trained on thousands of documents—like different resume formats or contract clauses—to accurately extract information, classify document types, or even predict candidate success based on past hiring data. This continuous learning enhances accuracy, especially when dealing with diverse or complex document sets encountered in global recruiting.

Natural Language Processing (NLP)

Natural Language Processing is a branch of AI that gives computers the ability to understand, interpret, and generate human language. NLP is crucial for processing unstructured text data found in resumes, cover letters, interview transcripts, and candidate feedback forms. For HR and recruiting, NLP enables systems to parse resumes for specific keywords, identify candidate sentiment, summarize long documents, or even screen applications for suitability by understanding the semantic meaning of skills and experience, rather than just matching exact phrases. This capability significantly reduces manual screening effort and improves candidate experience.

Document Processing

Document processing broadly refers to the actions involved in handling physical or digital documents throughout their lifecycle, from creation and capture to storage, retrieval, and disposal. In the era of automation, modern document processing increasingly involves intelligent tools that can automate many of these steps. For HR and recruiting, this means automating the intake of applications, extracting data from onboarding forms, managing employee records, and ensuring compliance documentation is correctly filed. Effective document processing is fundamental to maintaining a ‘single source of truth’ for employee data, a key differentiator for efficient operations.

Optical Character Recognition (OCR)

Optical Character Recognition is a technology that converts different types of documents, such as scanned paper documents, PDFs, or images captured by a digital camera, into editable and searchable data. For HR and recruiting, OCR is the foundational technology that allows systems to “read” information from diverse document formats, like scanned resumes or paper-based application forms. By transforming visual text into machine-readable text, OCR makes it possible for subsequent AI and ML processes to extract data, enabling automated data entry, digital archiving, and reducing the need for manual transcription, which is prone to error and time-consuming.

Intelligent Document Processing (IDP)

Intelligent Document Processing is an advanced form of document processing that combines OCR with AI technologies like Machine Learning and Natural Language Processing to extract, interpret, and process data from unstructured and semi-structured documents. Unlike basic OCR, IDP can understand the context of the information, handle variations in document layouts, and learn to identify data points even in new document types. For HR and recruiting, IDP can automatically process diverse incoming documents—from highly varied international resumes to complex compliance forms—extracting names, addresses, qualifications, and other relevant data with high accuracy, drastically reducing manual data entry and improving data quality across recruitment and HRIS systems.

Data Extraction

Data extraction refers to the process of retrieving specific information from a larger set of data or documents. In the context of AI and ML for document processing, this involves automated tools identifying and pulling out relevant data points such as a candidate’s name, contact information, work history, or educational background from a resume. For HR and recruiting, efficient data extraction is critical for populating applicant tracking systems (ATS), HR information systems (HRIS), and CRM databases without manual keying. This automation minimizes human error, accelerates the screening process, and ensures that critical candidate and employee data is captured accurately for further analysis and decision-making.

Automated Resume Parsing

Automated resume parsing is a specific application of NLP and ML where AI systems automatically extract and categorize information from resumes and CVs. This technology identifies fields like contact details, work experience, education, skills, and certifications, then structures this data into a usable format for an ATS or HRIS. For recruiters, automated resume parsing eliminates the laborious task of manually reviewing every resume, allowing them to quickly search for candidates based on specific criteria, match them to open roles, and build talent pipelines more efficiently. This saves significant time, improves candidate matching accuracy, and reduces unconscious bias often present in manual screening.

Sentiment Analysis

Sentiment analysis, a component of NLP, involves using AI to determine the emotional tone or attitude expressed in written text. While traditionally used in marketing, it’s increasingly valuable in HR and recruiting for understanding candidate feedback, employee survey responses, or even social media mentions. For example, sentiment analysis can gauge candidate experience during the hiring process based on survey comments, identify recurring themes in exit interviews, or assess general employee morale from internal communications. By quantifying subjective feedback, organizations can proactively address issues, improve their employer brand, and enhance overall talent management strategies.

Predictive Analytics

Predictive analytics in HR and recruiting uses historical data combined with AI and ML algorithms to forecast future outcomes or trends. This can include predicting candidate success, identifying top performers, forecasting employee attrition rates, or anticipating future talent needs based on business growth. For instance, by analyzing past hiring data, predictive models can help identify the characteristics of successful hires, allowing recruiters to target candidates with similar profiles. This data-driven approach shifts HR from reactive to proactive, enabling more strategic workforce planning, targeted recruiting efforts, and informed decision-making regarding talent development and retention.

Robotic Process Automation (RPA)

Robotic Process Automation involves using software robots (“bots”) to automate repetitive, rule-based digital tasks typically performed by humans. While distinct from AI, RPA often integrates with AI and ML for enhanced capabilities, particularly in document processing. For HR and recruiting, RPA bots can automate tasks like entering candidate data from an IDP system into an ATS, sending out standardized offer letters, scheduling interviews based on calendar availability, or updating employee records across multiple systems. This frees up HR professionals from monotonous administrative work, allowing them to focus on strategic initiatives and more meaningful candidate and employee interactions.

Large Language Models (LLMs)

Large Language Models are advanced AI models trained on vast amounts of text data, enabling them to understand, generate, and process human language with remarkable fluency and coherence. LLMs can perform a wide range of NLP tasks, from answering questions and summarizing text to generating creative content and translating languages. In HR and recruiting, LLMs can be utilized to draft job descriptions, personalize candidate outreach emails, summarize long resumes or interview transcripts, or even create sophisticated chatbots for applicant queries. Their ability to generate human-like text can significantly enhance communication efficiency and candidate engagement throughout the hiring lifecycle.

Generative AI

Generative AI is a type of artificial intelligence that can create new content, such as text, images, audio, or video, based on patterns learned from training data. LLMs are a prime example of generative AI for text. For HR and recruiting, generative AI extends beyond just understanding existing data; it can create new materials. This includes generating unique interview questions based on job roles, crafting compelling employer branding messages, personalizing candidate communications at scale, or even developing training modules and onboarding content. This capability allows HR teams to scale content creation, enhance personalization, and maintain brand consistency with less manual effort.

Candidate Matching Algorithms

Candidate matching algorithms are AI and ML models designed to identify the best fit between job seekers and open positions. These algorithms analyze various data points, including resume keywords, skills, experience, cultural fit indicators, and historical hiring data, to rank candidates based on their suitability for a role. For recruiters, these algorithms automate and accelerate the initial screening process, helping them quickly identify the most promising candidates from a large applicant pool. This technology improves hiring efficiency, reduces bias by focusing on objective criteria, and enhances the quality of hires by ensuring a stronger alignment between candidate profiles and job requirements.

Compliance Automation

Compliance automation involves using technology, including AI and ML, to streamline and ensure adherence to regulatory requirements and internal policies. In the context of document processing for HR and recruiting, this means automating the verification of licenses, certifications, and background checks, ensuring that all necessary forms are collected and properly stored according to legal mandates (e.g., GDPR, CCPA, EEOC). AI can help flag missing documents, identify discrepancies in data, or even analyze legal texts to ensure job descriptions and hiring processes remain compliant. This significantly reduces the risk of legal penalties, maintains data integrity, and frees up HR teams from tedious manual compliance checks.

If you would like to read more, we recommend this article: The Definitive Guide to CRM Data Protection and Recovery for Keap Users: Safeguarding Your Business Continuity