What is NLP? (Natural Language Processing)

Ever feel like a chatbot just gets you? It’s not magic. It’s science.

What is NLP? Meaning and definition

Natural language processing (NLP) is a type of technology that helps bots understand our questions, sifts through massive amounts of unstructured data and performs advanced communicative tasks.

NLP may be a part of your life in ways you don’t even realize—prompting chatbots, voice-operated GPS systems, Amazon’s Alexa and Apple’s Siri.

“It’s a little bit like a human. It goes through documents highlighting the words and forms of expression that are important as defined by the classification plan, allowing us to quantify the various concepts,” said Kelly Stone, an NLP expert for Dassault Systèmes’ Information Intelligence brand, NETVIBES.

Is NLP a new concept?

In recent years, we have become more accustomed to AI and the pairing of language and technology with the influx of large language models like ChatGPT, yet the science and process behind them, including NLP, has been around for decades.

Evolving from computational linguistics, NLP is a subfield of artificial intelligence and machine learning that uses algorithms to analyze textual relationships through language analysis and comprehension. Backed by statistical modeling, machine learning and deep learning, NLP models work hand in hand with computers and other devices to recognize, understand and generate text and speech.

While tools like ChatGPT are relatively new, NLP has been around since the mid-20^th century. It initially focused on rule-based systems in the 1950s and evolved into statistical models in the 1990s. Today, NLP use cases continue to grow, with many businesses using natural language processing tools to automate business operations and improve productivity.

Why is NLP important? The major benefits of NLP solutions

NLP tools have played a huge role in shaping the future of AI-driven communication technology. With the combined power of AI and NLP, major breakthroughs have occurred and have enabled an era of generative AI. Today, the advancement of NLP is being integrated into diverse fields such as life sciences and healthcare, finance, manufacturing and customer service due to its ability to facilitate human-computer interactions and rapidly and effectively take on tasks for workers.

NLP benefits

Faster business discovery: NLP uncovers hidden relationships between different pieces of content. Through text data retrieval, deeper insights and analysis, better-informed business decisions can be made.
Cheaper data processing: NLP automates data gathering and processes information with less manual effort, decreasing human labor costs. When businesses have a massive volume of unstructured text data to sift through, this information can be easily categorized and understood.
Task automation: NLP automates routine tasks such as customer support queries, content generation, and data extraction. This increases efficiency in business and production streamlining previously tedious tasks.
Language translation: This technology bridges communication gaps across languages, facilitating global interactions and commerce. NLP is breaking down the barriers in understanding across businesses.
Improved accessibility: NLP enables accessibility features like speech-to-text and text-to-speech for people with disabilities. It further improves users’ experience through customizing their preferences based on language and behaviors, enhancing engagement.
Enhanced search: NLP-powered search engines can understand the intent behind what a user is searching for – even when the query is complex or vague – to provide more accurate results.

3 different approaches to NLP

NLP can be divided into three main categories regarding its various tasks and applications. They are as follows:

Rules-Based NLP: Rules-based NLP was the earliest NLP application that answered simple if-then decision trees requiring pre-programmed rules. It can only provide answers in response to specific prompts.
Statistical NLP: Statistical NLP extracts, classifies, and labels elements of text and voice data and then assigns a statistical likelihood to each possible meaning of those elements. This form of NLP introduced the technique of mapping language elements such as words and grammatical rules.
Deep-learning NLP: Deep-learning NLP is the dominant mode of NLP that most users interact with. It uses huge volumes of raw unstructured data to become more accurate. Deep-learning NLP is a further evolution of statistical NLP.

Natural language processing examples and applications

NLP has become a part of most of our everyday lives, working to power search engine results such as Google, providing customer service chatbots, and driving with voice-operated GPS systems. NLP has had a growing role in enterprise solutions, streamlining and automating business operations, increasing employee productivity, and improving critical business processes.

NLP and healthcare

NLP applications in life sciences and healthcare are accelerating the process of reviewing and extracting relevant data from research papers, aiding in the discovery of new treatments and understanding of diseases. Chatbots and virtual assistants powered by NLP provide patients with information, schedule appointments, and offer preliminary health advice, enhancing patient engagement and accessibility.

NLP and data science

With NLP baked into its solutions, companies like NETVIBES are helping companies analyze large amounts of data and discover insights, monitor employee and customer experiences, and streamline business processes for previously tedious tasks.

NETVIBES is currently using NLP to help companies across industries overcome a number of unstructured data issues. For example, to review customer satisfaction surveys regarding a hotel for a client, which involves massive amounts of unstructured data. Categories are created, such as cleanliness, safety, and comfort. The model can then identify concepts in the customer reviews, ranking them as positive, negative or neutral within many subcategories. The ranking of each category is then produced as a percentage of negative and positive reviews and an overall customer satisfaction percentage is derived from these subcategories.

“About 90% of a company’s data is unstructured, making it very difficult to create value from,” said Stone. “NLP can analyze any unstructured data, ranging from customer experience data such as surveys, email complaints, and help companies to quantify what is driving satisfaction and make action plans to improve the customer experience. It can analyze change requests and quality reports to help companies optimize their internal processes and improve quality”.

Other NLP use cases

Spam detection: Large email services like Gmail use prevalent binary classifications to determine whether emails are spam or not. This allows for a better user experience, removing unwanted emails from our inboxes.
Online grammar checkers: Grammar checkers like Grammarly use such systems to provide better writing experiences, offering insights for grammatical corrections for writers to incorporate. These platforms have also helped teachers grade students’ essays in the classroom.

NLP tasks and techniques

NLP makes tedious tasks easier by taking massive amounts of unstructured data and making sense of it. Below are the most common NLP capabilities.

Coreference resolution: This involves identifying if and when two words refer to the same entity.
Part of speech tagging: NLP determines which part of speech a word or piece of text is based on its use and context.
Word sense disambiguation: This identifies a word’s meaning when it could have multiple meanings.
Named entity recognition (NER): NLP identifies words or phrases as useful entities when scanning large datasets. For example, a location or a person’s name.
Speech recognition: NLP can turn voice data into text by understanding intonation, nonstandard grammar, accents and more. Examples of speech recognition include Apple’s Siri and transcription services.
Machine translation: Machine translation software leverages NLP processing to convert speech or text to one language or another while retaining its contextual integrity.
Sentiment analysis: Sentiment analysis uses artificial intelligence to interpret the emotional tone behind contextual data. NLP tools will analyze the text for words that suggest anger, sadness, regret, and more.

What are the challenges of NLP?

NLP models remain imperfect and likely will never reach perfection, similar to how humans continue to learn language throughout their entire lives. Here are some of NLP’s challenges:

Biased training: If exposed to biased data in training, NLP, similar to other AI functions, will result in skewed answers. One way to overcome this is to train NLP functions on more diverse datasets. However, training datasets that are often scraped from the web are prone to bias.
Misinterpretation: With AI, there is always a risk of misinterpretation due to the lack of clear quality input involving mumbles, slang, or other mispronunciations. The input to the tool is critical to ensure misinterpretations are few and far between.
New vocabulary: When new words are invented or imported, NLP models can only make their best guess or admit they are unsure. These datasets need to constantly be updated and trained to ensure that new conventions and ways of speaking are incorporated into the NLP tool.
Ambiguity in language: When words and phrases have multiple meanings depending on the text, this ambiguity can make it challenging for NLP systems to interpret and may not always get it right on the first try.

“The main difficulty isn’t necessarily with technology, but rather the complexity of human language”, explains Stone. “We don’t always realize how complex language is until we’re trying to learn a second language or misinterpreting the meaning of a text due to missing context,” she said.

Addressing these challenges requires advancements in machine learning, natural language understanding, and the integration of broader contextual information to enhance the capabilities of NLP systems.

NLP technology with Dassault Systèmes

Here at Dassault Systèmes, NLP models understand human language at a deeper level and can unlock data previously hidden in unstructured text. NLP first gained traction here through the 2020 acquisition of Proxem, a France-based specialist in AI-powered semantic processing software. NLP has since expanded into the 3DEXPERIENCE platform, working alongside NETVIBES’ collaboration and intelligence applications. Together, it delivers a combination of rule-based natural language understanding, natural language processing, and machine learning technologies to see and understand the bigger picture.

Learn more about the 3D EXPERIENCE platform.

What is the future of NLP?

NLP models are paving the way for smarter and more personalized interactions, from healthcare to customer service to entertainment. This is a new era of seamless communication and collaboration in the digital age.

The future of NLP holds immense promise, driven by advancements in machine learning and AI. We can expect increasingly sophisticated models that can parse through large amounts of data to understand and generate human-like text, while comprehending context, tone, and nuance with greater precision. As NLP models continue to evolve, ethical considerations around data privacy, bias, and the responsible use of AI will become increasingly important, shaping how these technologies are integrated into society and our business.

More frequently asked questions about NLPs

What does NLP stand for?

NLP stands for natural language processing.

How does NLP work?

NLP works like a digital linguist, deciphering the intricate patterns and meanings embedded in human language. It starts by breaking down sentences into smaller components, like words and phrases, and then dives deeper to understand grammar, semantics, and context.

Through machine learning algorithms and vast datasets, NLP learns to recognize pattern usage, enabling it to perform tasks such as sentiment analysis, language translation, and speech recognition. By constantly evolving and learning from new data, NLP works to adapt to nuances and changes in language over time.

What is the connection between AI and NLP?

NLP and AI are deeply connected, with AI serving as the foundation of NLP. NLP can be thought of as a subfield of AI, where AI provides the intelligence and framework and NLP is a specific application that focuses on human language.

What is the difference between NLP and LLM?

The difference between NLP and LLM is that NLP is a broad field that includes many models and techniques for understanding language, while LLM is one specific type of model within NLP.

An LLM, also known as a large language model, is built on deep learning and trained to analyze massive amounts of text data. While an LLM is known for maintaining the context of long pieces of content, such as a document or an Excel sheet, an NLP model is often better at processing language on a more granular level.

What are the 5 steps in NLP?

The 5 steps or phases of NLP are:

Lexical analysis
Syntactic analysis
Semantic analysis
Discourse Integration
Pragmatic analysis

1. Lexical analysis

This step involves breaking down language into its smallest meaningful units, such as words, phrases, or morphemes. It categorizes these units into parts of speech and identifies their roles within the text, enabling machines to understand the foundational vocabulary of a language.

2. Syntactic analysis

This process examines the grammatical structure of sentences to ensure proper word arrangement. It identifies whether a sentence adheres to the rules of syntax, distinguishing between grammatically correct and incorrect constructions.

3. Semantic analysis

This stage focuses on interpreting the literal meaning of words and sentences. It evaluates the logical sense of language elements, ensuring that the conveyed meaning aligns with real-world concepts, even if the grammar is correct.

4. Discourse integration

This step connects sentences and phrases to derive context and resolve ambiguities. By analyzing preceding and subsequent text, it ensures that references like pronouns or implied subjects are correctly understood within the broader conversation.

5. Pragmatic analysis

This step interprets the intended meaning behind language, going beyond literal definitions. It considers context, tone, and cultural nuances to uncover implied meanings, such as metaphors, sarcasm, or emotional undertones.

Lanie ALBERT
Lanie Albert interned with Dassault Systèmes' Corporate Publishing team in 2024.

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