Natural Language Processing (NLP): Definition + Examples

Natural Language Processing (NLP) has evolved significantly from its rule-based origins in the 1950s to the advanced deep learning models of today. This technology allows machines to understand and interact using human language, impacting everything from language translation to virtual assistants.

For businesses, NLP is a transformative tool. McKinsey reports that AI technologies, including NLP, could add $13 trillion to the global economy by 2030. Investing in NLP solutions like virtual assistants can enhance your business efficiency by over 25%, according to Gartner. Read on to learn everything you need to know about NLP and the easiest way to get started.

What is Natural Language Processing?

Natural Language Processing Definition

Natural Language Processing is an artificial intelligence technology that equips machines with the ability to understand, process, and generate human language.

History of Natural Language Processing

In the 1950s, Alan Turing proposed that a machine could exhibit intelligent behaviors like a human, which set the stage for evaluating machine intelligence.

Early NLP efforts were dominated by rule-based systems, which relied on linguistic rules and syntax but struggled with the complexity of the natural language.

The 1980s and 1990s saw a shift towards statistical methods and introduced machine learning techniques.

The advent of deep learning in the 2010s revolutionalized NLP by leveraging large neural networks capable of learning from vast amounts of data. As a result, we are seeing breakthroughs such as OpenAI’s GPT-4o.

How Does NLP Work?

In a nutshell, NLP works through several steps to process human language:

1. When text is fed to the model, it gets broken down into smaller parts through the tokenization process.
2. Then, the structure of sentences is analyzed to understand how words relate to each other, known as parsing.
3. Through semantic analysis, the model works out the meaning of the text. During this process, word-sense disambiguation (WSD) plays a key role in determining the correct meaning of a word in context.
4. Using machine learning techniques such as attention mechanisms, NLP models learn from examples to understand language patterns and relationships.
5. Finally, the model can generate human-like, natural language responses based on what they understand.

Note that the first two steps of this process are known as “preprocessing techniques”, which help clean and standardize the text data, making it easier for NLP models to understand and analyze. In addition, the process of transforming raw text data into a numerical representation that can be used as input for ML algorithms is known as “feature extraction”.

What Is Time-Sensitive NLP?

Time-sensitive NLP (TS NLP) is a specific type of NLP that processes data in real-time or close to real-time. This model allows you to process data as it gets updated by the second and is great for monitoring news feeds, social media, and the chatbot itself. TS NLP consists of a hybrid form of NLP and machine learning to read and interpret text data and create long-form content such as articles or reports.

What is the Difference Between NLP, NLU, and NLG?

In simple terms, NLP is the broad field of enabling machines to process human language, while Natural Language Understanding (NLU) and Natural Language Generation (NLG) are subsets of NLP. The table below highlights the relationship between the three concepts:

NLP Models Vs. Large Language Models (LLMs)

While NLP models include a broader range of language processing techniques, LLMs represent a specific class of advanced neural network models for their size and scalability.

NLP in Business

NLP is revolutionizing business operations. According to McKinsey, high-performing companies using AI see significant value in product development, risk management, and supply chain optimization, leading to higher productivity and cost savings.

NLP Use Cases

• Customer Service: NLP-powered chatbots and virtual assistants provide instant, 24/7 customer support, handling queries, and resolving issues efficiently.
• Sentiment Analysis: Businesses can analyze customer feedback from social media, reviews, and surveys to understand public sentiment toward their products or services.
• Automated Document Processing: NLP automates the extraction and classification of information from documents such as invoices, contracts, and reports, reducing manual effort and increasing accuracy.
• Fraud Detection: NLP aids in detecting fraudulent activities by analyzing patterns in communication and transaction data, helping companies mitigate risks.

NLP Benefits for Businesses

Using NLP in business brings significant benefits, including increased efficiency, enhanced customer engagement, and cost reduction. By automating repetitive tasks, NLP frees up human resources and improves productivity.

NLP-driven chatbots enhance customer satisfaction by providing instant, personalized support, leading to higher retention rates. A case study highlights how integrating LLM-powered virtual assistants helped a retail company improve customer service interactions, resulting in a 20% increase in customer satisfaction scores and a 15% reduction in operational costs. These examples demonstrate how NLP can transform business operations, driving growth and competitive advantage.

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How Can I Get Started with NLP?

A study by the consulting firm, BCG, states that “integrating a third-party LLM-powered virtual assistant with a plug-in or other API is the quickest and easiest option to reach new customers in a generative AI world.”

The Best NLP Chatbot Builder: Voiceflow

The Voiceflow chatbot builder is your way to get started with leveraging the power of NLP! Trusted by 200,000+ teams, Voiceflow lets you create chatbots and automate customer service without extensive coding knowledge. Plus, it offers a user-friendly drag-and-drop platform where you can collaborate with your team.

Get started with a free account today!

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Frequently Asked Questions

What is Sentiment Analysis?

Using NLP and machine learning, AI can classify text with a “positive”, “neutral”, or “negative” sentiment. This is known as sentiment analysis. With sentiment analysis, AI can analyze text to understand different feelings, and even determine if needs need to be urgently addressed.

What are Some Popular NLP Applications and Tools?

Some popular NLP applications include Voiceflow for chatbots, Amazon Alexa for voice-activated control, ChapGPT for text generation, and HubSpot for CRM and sentiment analysis.

What are the Differences Between Rule-Based and Statistical NLP?

A rule-based NLP uses a series of rules to interpret data, with proper grammar and syntax being a high priority. Statistical NLP uses machine learning algorithms to analyze text data based on statistics and probabilities.

Statistical NLP is more accurate, yet more complex compared to rule-based NLP. While rule-based NLP is simple and straightforward, it relies on grammar and can only be generated in the language it was programmed for.

What Tasks Can I Relegate to an NLP Chatbot?

NLP chatbots can be used for several different tasks on behalf of individuals and companies. This includes customer support, appointment scheduling, order management, providing advice or suggestions, and updating information.