Natural language processing (NLP) is the field of AI that enables computers to understand and generate human language. While NLP aims to decipher everything from user intent, to synonyms and sarcasm, it often struggles with context-dependent meaning (polysemy), hallucination, and ‘common sense’ tasks that require a human’s lived experience.
However, with the benefit of structured knowledge (ontologies), NLP – particularly large language models (LLMs) – is getting better at bridging the gap between the nuanced world of human communication and the precise, rule-based world of machines.
What are the benefits of natural language processing?
NLP allows AI systems to process and make sense of unstructured text or speech data, which is how the majority of human knowledge is stored, including research papers and medical records. It makes technology more accessible to non-experts, and improves search by understanding the user’s intent better, rather than just relying on keyword searches for example. NLP also means that humans can have more productive and intuitive interactions with AI agents, and even collaborate with them, potentially making automated services more efficient and valuable.
What are the limitations of NLP?
NLP remains limited in grounding, reasoning, truth verification, and deep understanding. Hallucinations, where systems confidently make false statements or invent sources, are a persistent issue with generative LLMs that emphasise fluent text generation over verification.
NLP often misses ambiguity and implicit meaning. It can lose sight of context in long conversations, leading to semantic drift, where the meanings of words diverge, often unnoticed, from what they were intended to mean. NLP can display bias caused by the limitations and prejudices of the data it is given.
NLP can struggle with deeper reasoning such as the difference between correlation and causation, and may have difficulties with interpreting precise legal or technical language, especially when meanings are governed by overarching rules or guidelines. Also, most modern NLP models are ‘black boxes’, in that their outputs are difficult to explain (XAI) or audit properly, which restricts how much they can be trusted in regulated or critical domains.
How can NLP reliability be improved?
Structuring knowledge as ontologies, or deploying techniques such as RAG, are ways to improve the accuracy and reliability of NLP, while making their outputs more explainable. Ontologies are knowledge frameworks that define controlled vocabulary and set out how those words and concepts relate to one another. They can be used to provide rules and constraints that govern the outputs of an LLM, reducing the likelihood of hallucination.
Ontologies have long been used in critical domains such as medicine and advanced engineering, where errors have high costs, and where teams need to work across multiple disciplines and languages without misunderstanding shared concepts. But as the global economy digitalises, and more domains seek to introduce automation and vertical AI agents, reliability issues are slowing adoption.
As a result, interest in ontology generation and ontology-as-a-service platforms (OaaS) is intensifying.
What progress is NLP making?
A 2025 challenge designed to evaluate the effectiveness of large language models (LLMs) for ontology learning, highlighted that hybrid pipelines integrating commercial LLMs with domain-tuned embeddings and fine-tuning approaches achieved the strongest overall performance. Specialised domain models meanwhile improved results in biomedical and technical datasets.