What is NLP? (Natural Language Processing)

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

Natural language processing is a powerhouse transforming relationships between humans and technology. It helps bots understand our questions, sifts through massive amounts of unstructured data and performs advanced communicative tasks. NLP is trained on advanced algorithms to understand, manipulate and generate the human language.

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.

NLP uses algorithms to analyze textual relationships through language analysis and comprehension, while LLMs use deep learning to mimic human language and generate text. 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.

Natural Language Processing, defined

Natural language processing is a subfield of computer science machine learning. It enables computers to understand and communicate with human language. NLP evolved from computational linguistics which utilizes computer science to understand the very principles of language. NLP works with computers and other devices to recognize, understand and generate text & speech by combining computer-based modeling of human language with statistical modeling, machine learning, and deep learning.

The advancement of NLP is enabling its integration into diverse fields such as healthcare, finance manufacturing and customer service, enhancing human-computer interactions and shaping the future of AI-driven communication technology.

“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.  

Categories of NLP

NLP can be divided into three main categories regarding its various tasks and applications. When deciding what NLP works best for your business consider what task you aim to achieve. Below are three main subcategories of NLP:

• Rules Based NLP: Rules-based NLP were the earliest NLP applications that answered simple if-then decision trees requiring pre-programmed rules. They were only able to provide answers in response to specific prompts.
• Statistical NLP: Statistical NLP extracts, classifies, and labels elements of text and voice data and then assign 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 most users interact with which uses huge volumes of raw unstructured data to become more accurate. Deep-learning NLP is a further evolution of this statistical NLP.

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.

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 involve 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.

How is NLP transforming business?

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 critical business processes.

NLP is continuously being applied to diverse fields like retailing for customer service, chatbots and medicine, interpreting and summarizing electronic health records. Conversational agents such as Amazon’s Alexa also utilize NLP to listen to users and find answers.

In the healthcare field, NLP accelerates 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.

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

“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”.

How can your business use NLP?

NLP makes tedious tasks easier by taking massive amounts of unstructured data and make sense of it. But NLP does not stop there, here are additional values the technology holds according to insights from DeepLearning.AI.

• Linguistic tasks: 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 selects a word meaning for a word with many possible meanings.
• Named entity recognition: NLP identifies words or phrases as useful entities when scanning large datasets.
• 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 also have helped teachers grade students’ essays in the classroom.

Five major benefits of NLP

Once properly trained, NLP models can work rapidly and effectively and take on tasks for workers focusing their attention on other areas.

1. Faster business discovery: NLP uncovers hidden relationships between different pieces of content. Through text data retrieval, deeper insights and analysis enable better informed business decisions.
2. 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.
3. Automation of tasks: 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.
4. Language translation: This technology bridges communication gaps across languages facilitating global interactions and commerce. NLP is breaking down the barriers in understanding across businesses.
5. Improved accessibility: NLP enables accessibility features like speech-to-text and text-to-speech for people with disabilities. It further improves users experience through customization of user preference based on language and behaviors, enhancing engagement.

Why is NLP difficult?

NLP models remain imperfect and likely will never reach any level of perfection, similar to how humans continue to learn language their entire lives.

• Biased training: If exposed to bias 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: In AI there is also 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: With new words being invented or imported NLP can only make its best guess or admit it is 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 accurately interpret and generate human-like responses.

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

What is the future of NLP?

NLP is 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.

Here at Dassault Systèmes, NLP understands human language at a deeper level unlocking 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 Information Intelligence applications. This platform delivers a combination of rule-based natural language understanding, natural language processing, and machine learning technologies to see and understand the bigger picture.

NETVIBES uses NLP every day to support clients in making meaning from large amounts of data. They have also introduced their version of ChatGPT that will work internally trained on their specific datasets providing more accurate information to clients and businesses. Indeed, thanks to this kind of technology Dassault Systèmes will be able to offer conversational assistants for augmented employee based on a Retrieval-Augmented Generation (RAG) that takes into account all the knowledge and instanced information from the different application of the 3DEXPERIENCE platform.

The future of NLP holds immense promise, driven by advancements in machine learning and AI. We can expect increasingly sophisticated models that understand and generate human-like text and comprehend context, tone, and nuance with greater precision. As NLP continues 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.