Given they fundamentally operate in binary code, computer systems were once ill-suited to dealing with the complexities of human language. Developers leaned heavily on scripted inputs and outputs to mimic the form of human conversation, but the illusion was never convincing.

The emergence of natural language processing in recent years, though, has changed this course.

Natural language processing (NLP) enables computers to parse and create human language—actual words, not just binary 1s and 0s—and it’s changing the landscape of both the tech and business worlds.

In this introduction to natural language processing, we’ll dig into this vital topic for modern enterprises. We’ll provide a natural language processing definition, set out why NLP is so key for artificial intelligence, consider how it’s used across industries, explain natural language processing benefits and challenges, and anticipate what its future may hold. Let’s get started.

What is natural language processing and how does it work?

Natural language processing is an area of artificial intelligence that uses machine learning techniques to effectively analyze, understand, and even generate text in standard human languages. Users can type (or talk) to the AI platform as they would while communicating with another person, which helps to achieve greatly improved convenience and accessibility—you don’t need a computer science degree to interact with generative AI built on NLP frameworks.

The tech world has been trying to achieve reliable NLP for a long time, but older approaches relied heavily on static rules and dictionaries, making them inflexible and bad at dealing with nuance. Today’s NLP operates very differently to deliver far better results.

How NLP models are built

To get those reliable results, though, requires preparation and training—both from developers and from the AI itself. Here’s a rough breakdown of the steps needed.

Text preprocessing

Natural language must be suitably prepared before it can be processed. This will involve removing or replacing any non-standard characters (emojis, for instance), cleaning up formatting issues, and, most importantly, converting the text into manageable tokens (these may be phrases, words, subwords, or characters). This final step is undertaken by the generative large language model (LLM) itself.

Text representation

Once the text is preprocessed into tokens, the next step is to represent those tokens numerically. Necessary for semantic search (a big step beyond the keyword-based search that preceded it), this is usually achieved using word and sentence embeddings to turn each token into a vector. This step creates a set of numbers in which each one relates to an aspect, such as meaning, of the tokenized element. Once this is done, all the vectors can be placed in a vector space for easy comparison; the embeddings help to provide context for each representation to allow better understanding of nuance and the more complex aspects of grammar.

Pattern recognition

With the vectors in place, the model can start looking for patterns indicating content and context. Everything from a common phrase to an article topic can be determined in this way, allowing the model to extract the same things that a human could get from the original text and use them for in-depth analysis and answer generation.

Training the model

At this point, the system is merely a static pattern detector, capable of turning text into vectors but unable to do anything beyond that. Before it can start producing useful outputs (such as analysis or fresh content), it must be trained. This involves feeding the model example input-output pairs and tweaking its parameters until it starts consistently returning relevant results. Prompt engineering and fine-tuning might also occur at this stage.

Natural language processing methods

There are a variety of methods in the field of natural language processing that serve different purposes. The most common types include the following:

Rule-based NLP: When NLP was in its infancy, this approach—which relies upon set rules using conditional logic—was the only one available. While it can perform well in specific scenarios, it’s fallen out of favor due to more flexible and capable newer approaches.
Statistical NLP: Rather than following fixed rules, statistical NLP models function by analyzing training data and calculating the probability of words appearing in particular sequences and contexts. This makes them highly adaptable and allows them to generate new text.
Deep learning NLP: The advent of deep learning takes things a step further by granting the processing power needed to pick up on the subtleties common in natural language. This kind of NLP is so good at poring through vast quantities of training data that the content it produces is much more coherent than that from a more basic statistical approach. This method is primarily used by transformer models.
Hybrid NLP: There’s often value in combining approaches, and hybrid NLP methods do just that by adding rule-based logic to the more free-form probability-based NLP that’s so common today. This is a way of harnessing the strengths for scenarios in which neither is ideal, taking the former's precision and the latter's versatility to create a more robust whole.
Retrieval-based NLP: This type of NLP is focused on analyzing natural language queries and extracting the most pertinent answers from a pool of pre-existing responses, rather than creating entirely original answers. This saves time and computational resources, and can improve consistency. It’s useful for areas such as customer service, where information accuracy is essential and queries tend to be repeated.

The role of natural language processing in artificial intelligence

Very little of what we consider artificial intelligence today would be possible without advanced natural language processing. When the goal is to create systems that can act (and react) as humans do, it’s essential to have the ability to engage with real-world language without any need for translation.

This is particularly significant in AI deployments that involve directly interacting with people (e.g. voice assistants) or processing large amounts of written text (e.g. legal records). Through their increasing support of multimodal embeddings, such NLP models are getting closer to being able to use more resources, which will increase their adaptability when dealing with various kinds of data.

Use cases of natural language processing

NLP has already made its mark across a wide range of industries. Let’s look at some key use cases that show the capabilities of this GenAI technology.

Healthcare and life sciences

NLP is helping to refine clinical documentation by automatically extracting key patient information from unstructured text in electronic health records. Because it can rapidly analyze files and notes, it can retrieve everything from patient preferences and medical history to prescribed medications and courses of treatment. This frees up medical professionals to focus on other tasks.

It’s also changing how medical data is analyzed and used for further research. Paired with the incredible ability of AI models to find patterns in data, NLP is making it possible to cover massive pools of medical documentation and diagnostic results, pick out even the most subtle trends, generate personalized treatment plans, and propose new forms of medication for development and testing.

Finance

Data analysis is a core part of finance. Understanding market conditions requires bringing in language-based information from sources such as news feeds and trade publications, and signing off on financial moves requires a variety of checks. Using NLP, AI systems can quickly extract insights from all sources, aiding everything from risk analysis to transaction processing.

Additionally, NLP is helping to identify outliers in financial data for fraud detection. By analyzing transactional data (and drawing upon updates within regulatory filings), AI software can find inconsistencies or errors that indicate either sloppy work or fraudulent activity.

Insurance

NLP can help to make insurance companies' everyday paperwork less daunting by requiring reduced manual effort. AI systems can automatically extract relevant details from insurance documents and related customer communications (names, dates of birth, start/end dates, etc.) and assess claims at scale with a level of consistency and fairness that the human eye alone might not manage.

As in finance, NLP can help with fraud detection by analyzing transactional data and identifying unusual patterns, such as inconsistencies or errors. This can make it easier for insurers to identify possible fraudulent activity, helping to prevent errors and lowering the possibility of denying claims without cause.

Legal

In the legal world, NLP is helping to make compliance monitoring much more efficient. The law exists in a state of flux that makes it difficult for legal professionals to stay fully apprised, but AI systems can scan vast amounts of data at once. By feeding them as many volumes of legal texts and notices of regulatory shifts as you can, legal firms can create a superb reference that can answer questions quickly and accurately.

Having easy access to so much legal data also makes it much easier to carry out legal research while working on cases. A task like finding all precedents relevant to a case might take hours or days of manual effort, yet can be finished in mere seconds through the use of a suitable AI system. That means more time for legal professionals to work on case strategy.

Customer support

Given the relentless need for nuanced customer support, NLP is a natural fit. It is powering 24/7 virtual assistants that can do everything from providing order updates to helping people find the right products. Given that so many queries can be handled through text alone, AI applications with access to knowledge bases can effectively reduce the demand on customer support agents.

And then there’s the high-value area of customer happiness. Sellers stand to gain from understanding their customers better, and AI systems are excellent at poring through customer information (particularly feedback from a range of sources) and picking out notable sentiments and pain points that can be used to achieve improved support and service.

Media and entertainment

Content moderation is becoming evermore important online, and NLP is crucial to making this more manageable. AI-powered moderation systems can analyze text-based content in real-time across numerous digital platforms and flag (or delete) any policy violations. In doing so, they can serve to guard against the reputational and legal risks posed by letting content go unchecked.

NLP can also aid accessibility by generating real-time video captions for viewers who have hearing impairments or need to keep things quiet. Down the line, we may even see AI applications capable of analyzing and summarizing entire videos for the sake of convenience.

Public sector

Government agencies can use NLP to track public opinion by carrying out sentiment analysis on sources including social media posts, website comments, and surveys. In doing so, they can better understand what people are bothered by and what they’re hoping for, helping them make more practical decisions for citizens.

Public communication is another governmental area that’s helped greatly by NLP. An AI tool equipped with the right knowledge base resources through retrieval-augmented generation (RAG)—a way to connect external databases to an LLM—can efficiently and scalably answer questions about recycling collection days, planned roadworks, and anything else members of the public may be interested in.

Energy and utilities

In energy, NLP can enable smart predictive maintenance of key power systems by processing sensor data, technical specifications, and even maintenance reports logged by technicians. When managers can accurately anticipate operational lifespans, they can ensure that equipment is replaced with minimal disruption, lowering costs and preventing unnecessary downtime.

And with energy being a sector that’s subject to complex regulatory standards, NLP is highly useful for supporting legal compliance by helping companies align their strategies with the latest recommendations. By examining data about consumption trends and operational performance, it can also aid in the optimization of energy management plans.

Manufacturing

By examining production logs, sensor data, and technical documentation, NLP is helping to significantly improve operational efficiency in the manufacturing sector. Large amounts of unstructured data can be processed swiftly to identify trends and forecast when equipment is likely to break, allowing for predictive maintenance that improves ROI and minimizes downtime.

NLP is also frequently used for supply chain optimization, safety improvement, quality assurance, and regulatory compliance. For example, AI systems can easily spot process inefficiencies or identify product defects by analyzing data from production lines. To ensure efficient resource allocation, a supply chain can be streamlined by reviewing communication from suppliers, inventory logs, and contracts.

Main benefits of natural language processing

NLP offers a range of advantages that can transform how organizations handle and interpret text. Some of those advantages include:

Task automation: NLP can take care of any repetitive language-based tasks. It can retrieve requested information from processed files, generate template-based documents, and proofread (or even edit) content. With a reliable system, anything that doesn’t benefit much from human creativity can be handed over to AI, helping to reduce the potential for human error.
Advanced search capabilities: NLP-powered search engines can parse natural language queries, allowing them to deliver results that are more contextually relevant and thus of higher value to users.
Enhanced and intuitive user experience: Virtual assistants built with NLP continue to improve in their ability to provide interactions that feel natural and comfortable. Even when users know they’re dealing with AI, the natural language can help them feel understood and valued. At the same time, by better understanding user intent, NLP helps the system provide more accurate results.
Advances in research across industries: Whatever documents and databases you feed NLP models, they can pick out the tiniest patterns and yield insights that would be difficult to uncover manually. This is very important for helping research and accelerating innovation, especially in healthcare.
Better personalization and accessibility: NLP can effectively tailor content, recommendations and services to meet the needs of individual users, resulting in user-facing systems that are much easier to navigate and leave users more likely to return.
Efficient knowledge base creation: Creating a knowledge base traditionally requires a lot of effort and manual input. However, NLP can help automate the process of organizing, structuring, and making the information searchable.

Challenges of natural language processing

Alongside its benefits, natural language processing presents some challenges that can impact its effectiveness. Here are some to look out for, along with some recommended solutions.

Ambiguity and misinterpretation

NLP systems can find it hard to figure out elements of human language that are unclear, not least when there are multiple viable interpretations of the same text. If the system were to proceed based solely on assumption, it would risk producing irrelevant outputs.

To help address this, models need to be provided with as much context as possible, as surrounding text often clarifies ambiguous meanings. However, since context alone may not always resolve uncertainty, models should default to seeking clarification—just as humans ask questions when unsure in conversation.

Biased training data

Models trained on data that’s biased in some way will tend to carry that bias through to its outputs. To avoid this, it is important to be selective with training data, schedule regular data audits, and frequently run test queries to confirm that outputs are as unbiased as possible.

Evolving vocabulary and slang

Language is constantly changing, and the pace has only quickened in the internet age. New slang terms crop up and migrate between small communities before suddenly exploding in popularity. This is, of course, a problem for language models trained on old data. To address this, models should be regularly trained on newer data and exposed to human feedback to correct outdated language usage and recognize emerging terms.

Context and tone understanding

Human language relies heavily on tone, which can change the meaning of a simple word like “Hello” from a friendly greeting to a sarcastic remark. Since tone is difficult to convey through text, AI systems that process transcriptions may struggle to interpret intent with precision. While context helps, the best approach is to train models to analyze audio directly rather than relying solely on transcriptions, and storing tonal cues alongside other language elements.

Low-resource languages and dialects

While widely-spoken languages such as English or Spanish have vast amounts of training data available, this is not the case for languages spoken by smaller communities. As models need a lot of data to be trained, the only feasible solution to this problem entails the gathering of more linguistic data from less widely spoken languages. While this might require time and resources, it is a necessary effort on the part of dedicated communities to enhance technological advancements across cultures. Projects like Cohere For AI’s Aya model are helping to bridge gaps between people and cultures by expanding the number of languages covered by AI.

Long-range dependencies in text

Though NLP models have become good at tracking relationships between nearby words or sentences, they can struggle with connecting distant parts of a text. This can make it challenging to get consistent results for AI systems processing lengthy documents. A solution to this problem is to use advanced models such as transformers, which use specific mechanisms to maintain context over long spans of text. You could also segment super-long texts into smaller chunks for processing.

Privacy concerns

Since NLP systems in key industries such as healthcare must process sensitive information, people can understandably become concerned about how their data is being safeguarded. The solution is to commit to a thorough data policy with anonymization techniques and regulatory compliance at heart, as well as ensuring clear communication with users.

Future trends in natural language processing

Now that we’ve looked at the basics of NLP, let’s consider what the future holds. Here are some trends to look out for:

Multimodal understanding: NLP systems are already working towards this, and will increasingly draw upon numerous data formats (not just text, but also imagery, audio, video, etc.) to yield much broader insights.
Instant translation: We already have AI assistants that can process and produce text in many languages, but we’ll eventually have systems (linked to devices such as smart glasses or headphones) that can provide instant translation, making it possible to communicate anywhere in the world easily.
Better AI-generated content: As model architecture evolves, AI applications will consequently get better at generating progressively more accurate content. There may even be a future where it will be challenging to distinguish between human- and machine-generated text.
Advanced conversational agents: Next-generation virtual assistants will be capable of holding conversations that feel natural to the user. They’ll be responsive and adaptive, learning about the people they engage with and figuring out how to accommodate their preferences.
NLP for virtual reality and the metaverse: The VR market will grow with the evolution of hardware, and the demand for NLP-enabled AI systems to provide compelling virtual interactions will skyrocket. By facilitating natural, intuitive communication with virtual environments, avatars, and other users, NLP will be crucial in improving user experiences and increasing the accessibility and immersion of virtual worlds.

Natural language processing is a foundational element of the AI revolution, and it’s only due to advances in NLP technology that we’ve been able to see such incredible advancements in recent years. As we continue to see innovation and growth in the AI sector, we’ll keep a close eye on just how far NLP can take us.

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Natural language processing (NLP): A practical guide