What is natural language processing (NLP)?
Natural language processing (NLP) is a type of artificial intelligence (AI) dedicated to the interaction between systems and human language. When you communicate with an AI-powered chatbot or a voice assistant, NLP is the technology that interprets your speech or text, understands the intent behind your words, and generates appropriate responses.
Evolution of NLP
NLP’s roots began in the 1950s, when pioneers in computational linguistics used rule-based systems to process language. These systems, known as symbolic NLP, relied on manually crafted algorithms to analyze syntax, grammar, and sentence structure.
As machine learning (ML) grew in the 2000s, researchers came up with other NLP approaches:
- Statistical NLP: Uses algorithms that analyze and learn patterns from large datasets rather than depending on manually crafted rules
- Hybrid NLP: Combines symbolic and statistical methods and often supports complex techniques like sentiment analysis and machine translation
You might be wondering: Is NLP machine learning? The simple answer is no. But advanced NLP uses machine learning algorithms to process raw text. For example, you can use both technologies to summarize content automatically, which saves time and helps extract key points from extensive contracts, research papers, or meeting notes.
NLP vs. LLM: Key differences
Large language models (LLMs) and NLP both deal with language understanding and generation, but these areas of AI have different purposes. Here’s a breakdown:
| Aspect | NLP | LLM |
| Definition | A broader branch of AI that covers various techniques to understand, interpret, and generate human language | Advanced AI technology within NLP that specializes in generating human-like text |
| Trained data requirements | Smaller datasets in the case of traditional NLP methods and larger datasets when it comes to ML-based methods | Use of large amounts of data to achieve high performance |
| Flexibility | Task-specific, often requiring fine-tuning — adjusting a pre-trained model with additional training on a task-specific dataset | Ability to perform multiple tasks without task-specific fine-tuning |
You can combine NLP and LLM to enhance language understanding and generation capabilities. For instance, in chatbots that integrate generative AI (GenAI) and conversational AI, NLP processes and interprets your input, while LLMs generate accurate and contextually relevant responses.
Expanding the use of AI in your business? Find ideas in our guide to conversational AI.
NLP techniques
NLP employs a range of techniques to support tasks like AI-powered content creation, question answering, language translation, and more.
Examples of natural language processing techniques include:
- Tokenization: The process of breaking down text into words or sentences to better understand its basic components
- Parsing: The analysis of the grammatical structure of a sentence to identify relationships between words and learn how they form phrases or clauses
- Lemmatization: The standardization of words to ensure the resulting form is valid in the language — for example, “strategies” becomes “strategy,” and “management” becomes “manage”
- Stemming: The removal of prefixes or suffixes to reduce words to their root form, standardizing their variations — following the same example, “strategies” becomes “strateg,” and “management” remains “manag”
- Sentiment analysis: The assessment of the emotional tone of a piece of text, classifying it as positive, negative, or neutral
- Machine translation: The automatic conversion of speech or text from one language to another, leveraging NLP algorithms and deep learning models to understand context, grammar, and meaning across languages
Many AI systems integrate these techniques to improve the accuracy of NLP tasks. Let’s say you implement an Intelligent Content Management solution to gather insights from unstructured data. The system will likely rely on techniques such as tokenization, parsing, and sentiment analysis to extract relevant information, understand its context, and categorize it accordingly.
Explore the top benefits of content intelligence for businesses.
Why is NLP important?
As enterprise AI adoption takes off, you should look for new ways to expand the technology’s applications in your business. According to Asana’s State of AI at Work, on average, workers use AI for five different tasks — the most popular use cases include email generation, information summarization, content creation, technical writing, and ideation and brainstorming. With natural language processing, you help teams find unstructured data in storage or draft content in real time, extending AI capabilities across departments.
Here are the top benefits of NLP:
- Increased efficiency and productivity: Market.us reports that large enterprises hold more than 62.1% of the overall NLP market share. NLP technology helps data-heavy businesses streamline data lifecycle management with instant content generation, summarization, and retrieval, saving hours of manual work and making information accessible faster.
- Enhanced customer experience: Technologies powered by NLP, such as chatbots, virtual assistants, and sentiment analysis tools, identify the intent behind customer queries in real time. This capability empowers teams across departments, from customer support to sales, to address clients’ concerns faster and more effectively, fostering loyalty and trust.
- Improved data analysis: According to a Box-sponsored IDC white paper, 50% of survey participants report their company’s unstructured data is mostly or completely siloed. With NLP processing tools, you break down these silos by powering search, retrieval, and analysis of documents. This way, you can increase the value of your content, extracting insights from previously inaccessible information.
Discover how the landscape of enterprise AI adoption is evolving.
Natural language processing in AI: Examples by industry
Fortune Business Insights forecasts the global natural language processing market size will reach $158.04B by 2032, with a CAGR of 23.2% from 2024 to 2032. Driven by the rise of cloud-based solutions, NLP use cases benefit from the rapid advancements of AI and ML, powering more precise language processing and analysis.
Let’s review how to get the most out of this technology.
| Industry | NLP applications |
| Healthcare |
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| Retail |
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| Financial services |
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| Engineering |
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| Legal |
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| Education |
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| Media and entertainment |
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How does NLP work?
Through AI-powered platforms or NLP tools, you incorporate different techniques into your business applications or workflows. To understand how this process typically works, let’s review the critical steps of an NLP pipeline.
1. Data collection and preparation
The first step involves gathering raw text data from various sources, such as documents, presentations, or reports within your cloud data storage.
The system then cleans the data to remove irrelevant information (special characters, for example) and standardizes it by converting text to lowercase, correcting typos, and adding relevant metadata to improve analysis.
2. Part-of-speech tagging
Part-of-speech (POS) tagging involves recognizing and labeling the grammatical roles of words in a sentence, like nouns, verbs, adjectives, and adverbs. The goal is to comprehend the role of each word in the sentence.
Just like in search engines, if you enter “marketing strategies for small businesses” in your document management system, the platform applies POS tagging to retrieve reports, presentations, and other files that specifically mention marketing strategies for this audience.
3. Named entity recognition
Named entity recognition (NER) focuses on detecting named entities in the text, such as the names of organizations, people, dates, locations, or financial values. This step lets you classify and extract important information to streamline tasks like customer profiling, document categorization, and content tagging.
4. Syntax and semantic analysis
After preparing and categorizing data, the next step is to help natural language processing tools analyze the structure and meaning of text. While syntax analysis — also known as the parsing technique — understands the relationships between words in a sentence, semantic analysis goes beyond grammar to understand the meaning behind those words and phrases.
5. Sentiment and context analysis
Using AI to analyze feedback from customers allows sentiment and context analysis to indicate the tone of the text and the intent behind it. For example, if you use these techniques to assess a conversation transcript with a client, your customer support team can:
- Discover if the client’s overall tone is positive, negative, or neutral
- Recognize whether the client’s intent is to seek assistance, make a purchase, or escalate a concern
- Highlight words or phrases that reveal strong emotions like “frustrated” or “helpful”
- Categorize the conversation content by topics, such as billing, product features, or service complaints
6. Model training and evaluation
At this stage, ML models train on the processed data to perform specific tasks, such as identifying sentiment or detecting grammar mistakes to speed up the content review process. Both ML and NLP models learn from data and evaluate their accuracy using metrics like precision and confusion matrix, which assess the errors your model makes.
7. Output generation
The final step involves generating outputs based on the insights from the analysis. This could include a sentiment score, text classification, or an AI-powered summary, depending on the NLP task.
Integrate NLP technology into your business with Box AI
Box provides document creation, storage, sharing, collaboration, and more on a centralized platform where teams securely manage the content lifecycle. The Intelligent Content Cloud offers advanced AI capabilities — including NLP technology to analyze and interpret text — allowing you to seamlessly extract the full value of your information.
Integrate Box AI into your workflows to create, summarize, personalize, search, and retrieve relevant information in real time. Empower your team to ask questions and get instant answers based on your documents, leveraging AI-powered insights to make decisions with confidence.
With Box, you connect your content across 1,500+ applications, enabling every department to complete tasks faster without leaving the app — all with enterprise-grade security and compliance features that protect your data as it moves across your tech stack.
Contact our team and discover how to get value from your data with natural language processing.
While we maintain our steadfast commitment to offering products and services with best-in-class privacy, security, and compliance, the information provided in this blog post is not intended to constitute legal advice. We strongly encourage prospective and current customers to perform their own due diligence when assessing compliance with applicable laws.