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What is natural language understanding?

Mara Ellis August 12, 2021

Definitions

Natural language understanding (NLU) tackles the challenges experienced by computers by making sense of how people actually use language. At the most basic level, this means identifying how words fit together in a sentence. At a deeper level, it means understanding meaning, recognizing, for example, that the word ‘bark’ could refer to a sound a dog makes or the outer layer of a tree, depending on context.

As one foundational study ‘Natural language processing: state of the art, current trends and challenges’ explains, “Natural language processing has recently gained much attention for representing and analyzing human language computationally, and it has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering.”

NLU also goes beyond literal wording to interpret intent, taking into account tone, situation, and implied meaning. Early systems depended on rigid, rule-based methods, but as language proved far too complex for fixed rules, researchers turned to statistical approaches.

“Natural language processing is a tract of artificial intelligence and linguistics, devoted to making computers understand statements or words written in human languages,” the same research notes.

By training models on large collections of real text, NLU systems became better at handling informal speech, incomplete sentences, and the natural flow of conversation. As voice technology advanced, NLU evolved again, learning to cope with pauses, hesitations, and background noise by using cues like rhythm and emphasis to improve accuracy.

In email environments, these capabilities have become especially valuable for turning large volumes of unstructured messages into actionable information. NLU is used to extract details from emails and to detect subtle language patterns, such as uncertainty, urgency, or negation, in statements like ‘this isn’t an approved request.’ Techniques, including shallow parsing, word sense disambiguation, and relationship mapping, help transform everyday correspondence into structured data.

Why understanding is more than keyword matching

A simple change in sequences, like ‘Ram beats Shyam’ versus ‘Shyam beats Ram’, completely reverses the message, even though the same words are used. This is where syntactic parsing makes a difference, revealing meaning through sentence structure and the way phrases are formed.

Semantic analysis resolves ambiguity, helping systems understand what a sentence is really about. It can recognize that a passage refers to ‘movies’ through related words like ‘actor’ or ‘script,’ even if the word itself never appears.

Pragmatic analysis adds yet another layer by focusing on what people mean, not just what they say. Using actual knowledge and situational cues, it interprets implied messages that are never stated outright, something keyword scanning simply cannot do.

As one review from the Yearbook of Medical Informatics on health-related texts explains, “The field has long advanced beyond the simple keyword search functionalities that are widely implemented in commercial EHR systems,” showing how modern language systems look for concepts rather than exact words to capture meaning from unstructured records.

Advanced language systems can recognize medical entities expressed through synonyms, broader terms, or more specific descriptions, making them far more reliable than rigid keyword lists that often miss these variations. At the discourse level, language processing also connects ideas across sentences, resolving references such as a pronoun like ‘he’ pointing back to ‘Ram.’ Without this ability, summaries and extractions lose much of their meaning.

Where machines excel (scale, speed, consistency)

Modern ML systems can process thousands of emails a day by automatically learning patterns in language. Instead of relying on endless hand-written rules, they use combinations of models to spot threats across huge and constantly changing datasets. This allows generative AI tools to create realistic security alerts and even training data that reflect actual attack behavior, helping organizations stay prepared for new and evolving threats.

An editorial from Frontiers in Artificial Intelligence states that more than 600 million cyberattacks are detected every single day, with phishing and identity-based attacks leading the list. That volume makes manual review impossible, which is why generative AI tools are now used to create realistic security alerts and training data

Techniques like one-dimensional CNNs and hybrid CNN-BiGRU models can analyze email headers, message content, and attachments almost instantly. Studies show these systems can reach accuracy rates close to 99% in just milliseconds per email, far faster than any manual review process. In practice, this means security teams can respond to potential threats in real time, not hours later.

Consistency may be the most underrated advantage. Machine learning applies the same standards to every message, even when attackers try to hide behind obfuscation or clever wording.

Where humans still lead (reasoning, empathy, moral judgment)

Humans still lead generative AI in email security when it comes to reasoning, empathy, and moral judgment. In reasoning, humans excel at common-sense inference and contextual adaptation, detecting subtle phishing tactics like culturally specific social engineering or novel attack variants that evade pattern-based models. Generative AI struggles with ‘zero-day’ threats requiring logical deduction beyond statistical correlations, where human oversight identifies intent in ambiguous phrasing.

There is also the fact that, as per a PLOS Digital Health study, “Data generated through AI-based technologies can suffer from hallucination, where the models produce inaccurate or nonsensical data that may undermine the validity of analyses. Moreover, biases in generated data can emerge from incomplete or skewed assumptions.”

Empathy allows humans to gauge emotional manipulation, such as personalized urgency exploiting sender relationships, which AI cannot authentically assess without true interpersonal understanding, risking overlooked psychological ploys in spear-phishing. Moral judgment is needed for ethical triage; humans weigh privacy, false positives harming legitimate communications, and proportionality in flagging gray-area content, areas where AI risks bias propagation or overreach without human values.

The reality of its application in generative AI email security

Some of the newest deep learning systems are incredibly good at spotting malicious emails. Models that combine one-dimensional CNNs with Bi-GRU layers can scan everything from headers and message text to attachments and pick up on patterns most people would miss. In controlled tests like the one noted in a Sensors study, these tools have reached accuracy levels close to 99.7%, with precision and F1 scores that outperform many traditional spam filters, especially on well-known datasets like the Phishing Corpus.

Systems like the one offered by Paubox’s new generative AI feature can flag threats almost as soon as an email arrives, even when the message is generated by AI and designed to look completely legitimate. Still, they aren’t foolproof. Just like older spam filters, modern models can be tricked. Attackers constantly experiment with ways to slip past detection by tweaking wording, formatting, or metadata, small changes that sometimes exploit blind spots in the model.

FAQs

What is generative AI?

Generative AI is technology that creates new content, like text, images, or audio, by learning patterns from large amounts of existing data and using them to produce realistic, original output.

What are the components that make generative AI work?

Generative AI relies on large datasets, powerful machine-learning models, and carefully designed algorithms that work together to understand patterns and turn them into new content.