Natural Language Processing (NLP) is the branch of artificial intelligence that enables machines to understand, interpret, and generate human language. This technology combines computational linguistics with machine learning to grasp not just words, but the intentions and emotions behind them. In this article, we'll explore how NLP works, its key benefits, practical applications, and techniques you can use.

Understanding natural language processing

Natural language processing definition

Natural Language Processing (NLP) is artificial intelligence technology that enables computers to understand and interact with human language. NLP combines computational linguistics with machine learning models to process text and speech, extracting meaning, intent, and sentiment from human communication.

The history and evolution of NLP

The roots of NLP can be traced back to the 1950s, with the famous Turing Test, which challenged machines to exhibit intelligent behavior indistinguishable from that of a human. From early machine translation projects like IBM's Automatic Language Translator to modern, sophisticated algorithms used in AI chatbots, NLP has grown exponentially alongside advancements in computing power and machine learning.

Since then, NLP has evolved significantly, propelled by advances in AI and computational theories. Today, it integrates multiple disciplines, including computer science and linguistics, striving to bridge the gap between human communication and computer understanding.

Intercom Fin, an AI chatbot. Source: Intercom

Benefits of natural language processing

NLP delivers measurable value across four key areas:

• Automation: Automates language-based tasks like document processing and customer support

• Insights: Extracts patterns and sentiment from unstructured text data

• Search: Powers intelligent search that understands user intent

• Experience: Enables natural conversations with AI systems

Business efficiency and automation

NLP automates repetitive, language-based tasks, including:

• Automatically routing customer support tickets

• Summarizing reports into key points

• Extracting information from documents

• Reducing manual data entry and processing time

Enhanced data analysis and insights

Vast amounts of valuable information are locked away in unstructured text like customer reviews, social media comments, and support emails. For example, in the healthcare field alone, an estimated 80% of medical data remains unstructured after it is created. NLP unlocks this data by analyzing it for trends, sentiment, and emerging themes, providing businesses with deep insights into customer satisfaction and market perception that would be impossible to gather manually.

Improved search and information retrieval

Traditional keyword search can be frustrating. NLP powers more intelligent search systems that understand the user's intent and the context behind a query. This makes it faster and easier for employees to find the exact information they need within a company's knowledge base, boosting productivity and reducing time wasted searching.

Better customer experiences

NLP enables businesses to provide instant, 24/7 support through intelligent chatbots that can understand and resolve common customer issues. It also allows companies to analyze customer feedback at scale, leading to more personalized interactions, proactive service improvements, and a stronger overall customer relationship.

How does NLP work? Looking at NLP models

NLP involves several stages of processing to understand human language. The initial step is to break down the language into shorter, elemental pieces, try to understand the relationship between them, and explore how these pieces work together to create meaning.

Types of NLP models

Navigating through the world of Natural Language Processing, you'll find a fascinating array of models each designed to bridge the gap between human communication and machine understanding. Let's dive into the main types of NLP models that help machines comprehend and interact with human language.

Rule-Based Systems

Rule-based systems are the earliest form of NLP models, relying on sets of hand-coded rules to interpret text. These systems are fairly straightforward: you input specific instructions, and they follow them to the letter. They're great for structured tasks where the rules don't change much, like answering frequent questions in a customer support chat.

Example: Imagine a chatbot designed to handle common customer queries. If someone asks, "How do I reset my password?" the bot responds with predetermined instructions based on the rules it's been given. However, if you ask it a question that it hasn't been specifically programmed to handle, the system might not know how to respond.

Statistical Models

Statistical models use mathematical techniques to infer the structure and meaning of language. They don't learn rules like their rule-based cousins; instead, they look at data and statistically infer what's most likely to be true. They're like detectives, piecing together clues (data) to form an understanding of language patterns.

Example: Consider how your email sorts out spam. Statistical models analyze the words commonly found in spam and legitimate emails and use this data to classify incoming messages. This method isn't perfect, but it's pretty good at making educated guesses, significantly reducing the clutter in your inbox.

Machine Learning Models

Machine learning models for NLP are more flexible than rule-based or traditional statistical models. They learn from their experiences, adjusting their methods as they digest more and more data. It's like they start with a basic understanding of a language and get smarter over time, making them incredibly versatile and increasingly accurate.

Example: Sentiment analysis tools on social media platforms use these models to gauge public opinion about a brand. These tools get better at detecting subtle nuances in language—distinguishing between genuinely positive comments and sarcastic ones, for example—as they analyze more posts.

Neural networks and transformers

Neural networks, particularly deep learning models, have significantly advanced NLP fields by enabling more complex understandings of language contexts.These models use complex algorithms to understand and generate language. Transformers, for instance, are adept at grasping the context from the entire text they're given, rather than just looking at words in isolation.

Example: Google's BERT is a standout transformer model that has revolutionized how machines understand human queries, with research demonstrating that its models outperformed ELMO and Flair, other popular language models, across multiple datasets. Whether you're asking a simple question or seeking deep insights, BERT considers the full context of words in your query, ensuring that the responses are not just accurate but also relevant to your specific needs.

These models showcase the breadth and depth of techniques in the field of NLP, from the rigid but reliable rule-based systems to the highly sophisticated and contextually aware transformers. As we continue to develop these technologies, the potential for even more nuanced and effective communication between humans and machines is vast and exciting.

Exploring natural language processing techniques

Diving into natural language processing reveals a toolbox of clever techniques designed to mimic human understanding and generate insightful interactions. Each method plays a crucial role in dissecting the intricacies of language, enabling machines to process and interpret text in ways that are meaningful to us humans. Let's walk through some of these key techniques and see them in action.

Tokenization

Think of tokenization as the meticulous librarian of NLP, organizing a chaotic array of words and sentences into neat, manageable sections. This technique breaks down text into units such as sentences, phrases, or individual words, making it easier for machines to process. Whether analyzing a novel or sifting through tweets, tokenization is the first step in structuring the unstructured text.

Example: In customer feedback analysis, tokenization helps parse customer reviews into sentences or terms, allowing further analysis like sentiment scoring or keyword extraction. For instance, the review "The product is great, but the service is terrible!" would be split into tokens like "product", "great", "service", and "terrible", each analyzed separately for sentiment.

Part-of-Speech tagging

If tokenization is a librarian, part-of-speech tagging is the grammar teacher of the NLP world. It involves scanning words in a sentence and labeling them according to their roles: nouns, verbs, adjectives, etc. This tagging helps clarify how words relate to each other and form meaning, which is critical for understanding requests and generating responses.

Example: In voice-activated AI assistants, part-of-speech tagging helps determine the function of each word in a command, such as distinguishing between "light" as a noun in "Turn on the light" versus "light" as an adjective in "I want my coffee light." This clarity is essential for the assistant to perform the correct action.

Named entity recognition (NER)

Named entity recognition (NER) is the detective of NLP techniques. It scans text to locate and classify key information into predefined categories like people, organizations, locations, dates, and more. NER is invaluable for quickly extracting essential data from large texts, making it a favorite in data extraction and business intelligence.

Example: Financial news articles are gold mines of information that NER helps extract efficiently. For instance, from the sentence "Apple Inc. announced its Q3 earnings on October 30 in Cupertino," NER would identify "Apple Inc." as an organization, "October 30" as a date, and "Cupertino" as a location. This information can be used to populate financial databases or trigger trading algorithms.

Sentiment analysis

Sentiment analysis is the emotional radar of NLP. It detects the mood or subjective opinions expressed in text, classifying them as positive, negative, or neutral. This technique is particularly popular in social media monitoring, marketing analysis, and customer service, as it provides insights into public sentiment and customer satisfaction.

Example: A company could use sentiment analysis to monitor social media mentions of its brand, quickly identifying and categorizing user opinions. In one public health study, for example, an NLP model reviewed 144,101 tweets from Japan to identify relationships between public views and vaccination levels. For example, the tweet "Absolutely love the new update!" would be marked as positive, while "Frustrated with the new layout!" would be classified as negative. This feedback allows companies to gauge customer reactions and adjust strategies accordingly.

These NLP techniques illustrate just how machines can be taught to understand not only the structure of language but also its meaning and emotional tone. By leveraging these methods, businesses and developers can create richer, more interactive experiences that feel both personal and efficient. As we continue to refine these techniques, the potential for creating systems that truly understand and interact with us on a human level becomes more and more tangible.

Natural language processing examples across industries

NLP solves real-world problems across industries:

• Healthcare: Processes clinical notes and patient records to extract diagnoses, medications, and symptoms

• Finance: Analyzes documents for risk assessment and fraud detection

• Customer Service: Powers chatbots and automatically routes support tickets

• Marketing: Performs sentiment analysis and optimizes content for search engines

Healthcare applications

In healthcare, NLP helps by processing unstructured clinical notes and patient records. For instance, a study of over 76,000 patients demonstrated that NLP could extract psychosocial factors like isolation and financial insecurity from chart notes to predict their impact on hospitalization. It can extract key information like diagnoses, medications, and symptoms, which helps clinicians make faster, more informed decisions and supports large-scale medical research.

Financial services use cases

The financial industry uses NLP to analyze documents for risk assessment, ensure regulatory compliance, and detect fraudulent activity. By parsing news articles, reports, and transaction data, NLP models can identify patterns and flag potential issues that would be missed by human analysts.

Customer service automation

Across all industries, NLP is the engine behind customer service automation. It powers chatbots that provide instant answers to common questions, automatically routes support tickets to the correct department, and analyzes conversation transcripts to ensure quality and identify areas for agent training.

Content and marketing optimization

Marketers use NLP to perform sentiment analysis on social media to gauge brand perception and customer opinions. It also helps in optimizing content for search engines by better understanding user query intent and can even assist in generating ideas for new articles or campaigns based on online discussions.

Decoding the meaning: What NLP means for businesses and individuals

Natural language processing uses in business

NLP is revolutionizing business practices across various industries by enhancing how companies process human language. Here are some key applications:

• Business intelligence: As we learned earlier, companies use NLP to monitor brand sentiment on social media, automate customer support via chatbots, and unlock insights from customer feedback.

• Healthcare: NLP streamlines healthcare by processing patient data and clinical notes for faster diagnostics and personalized patient management, helping medical professionals make informed treatment decisions.

• Financial services: In finance, NLP is crucial for parsing complex documents for risk assessment, ensuring compliance with regulations, and detecting fraudulent activities through pattern recognition in transaction data.

NLP uses for individuals

Hey Siri—how can I use natural language processing in my daily life? For individuals, NLP provides tools that greatly enhance personal productivity and access to information. Here are a few ways how NLP brings sophisticated technology into everyday use:

• Personal Assistants: Voice-activated assistants like Siri, Alexa, and Google Assistant leverage NLP to understand and execute a wide array of commands, from setting reminders to managing smart homes, enhancing daily convenience and efficiency through natural language.

• Language Translation Services: NLP-driven tools such as Google Translate break down language barriers in real-time, translating text and providing video subtitles to make information universally accessible and support more inclusive interactions.

• Educational Tools: NLP transforms educational software by automating response grading and customizing learning experiences, as seen in apps like Duolingo, which adjusts content based on the user's progress and provides instant feedback to improve language skills.

• Accessibility Features: For individuals with disabilities, NLP facilitates access to technology via text-to-speech and speech-to-text conversions, enabling visually impaired users to consume digital content and those with motor impairments to navigate devices using voice commands

Apple's voice-activated assistant, Siri. Source: Apple

Getting started with natural language processing

Ready to explore NLP? Here are practical ways to get started:

• Read How-To Guides: Start with tutorials on Towards Data Science and Medium

• Explore Libraries: Experiment with NLTK and spaCy for hands-on learning

• Take Online Courses: Try Coursera, Udemy, or Hugging Face courses

• Practice with Data: Use real datasets from Kaggle or UCI Machine Learning Repository

• Read Books: Study "Speech and Language Processing" by Jurafsky and Martin

These resources provide both theoretical knowledge and practical skills for applying NLP effectively in real-world scenarios.

The future of NLP

NLP is evolving rapidly. Key developments ahead include:

• Enhanced Understanding: Deeper comprehension of context, irony, and emotional subtleties

• Cross-Disciplinary Integration: Incorporating psychology and neuroscience for more intuitive responses

• Multilingual Expansion: Supporting more languages and dialects globally

• Ethical AI: Eliminating biases to ensure fair analysis, which is critical for preventing issues like one at Amazon, where a recruiting algorithm was discontinued after discovering gender bias.

• Real-Time Processing: Instant language processing for live translation and moderation

These advances will make digital experiences more seamless, inclusive, and respectful of ethical standards.

Building your AI source of truth with NLP

Understanding NLP is the first step. The next is putting it to work safely and effectively. For enterprises, the real value of NLP is unlocked when it's powered by a trusted, auditable knowledge base—an AI Source of Truth. This ensures that every answer generated, whether for an employee or a customer, is accurate, permission-aware, and based on your company's verified information.

By connecting your company's knowledge, Guru's context-aware intelligence engine uses NLP to deliver reliable answers where you work. See how you can build a trusted layer of truth for your people and AI. Watch a demo.