What is the difference between NLP and NLU?

Natural language processing is the broad field covering all computational interactions with human language. Natural language understanding - NLU - is a subset of NLP specifically focused on comprehension - extracting meaning, intent, and context from text. NLP also includes natural language generation, which is the production of human-readable text from data.

How is NLP used in search engines?

Search engines use NLP to understand what users mean, not just what they type. Google's BERT and MUM models use NLP to interpret query intent, match queries to relevant content, and understand the semantic meaning of web pages. AI search engines like ChatGPT and Perplexity use NLP extensively for query understanding, content evaluation, and answer generation.

What programming languages are used for NLP?

Python is the dominant language for NLP development, with libraries like spaCy, NLTK, Hugging Face Transformers, and PyTorch. Java is used in enterprise NLP applications. R is used for text analytics in data science. Most modern NLP work leverages pre-trained transformer models rather than building from scratch, reducing the code required significantly.

What Is Natural Language Processing (NLP)?

Natural language processing (NLP) is a branch of artificial intelligence that focuses on enabling computers to understand, interpret, generate, and interact with human language in meaningful ways. NLP powers the technology behind AI search engines, voice assistants, chatbots, machine translation, sentiment analysis, and the large language models like GPT-4 and Claude that millions of people use daily. The global NLP market was valued at approximately $24 billion in 2024 and is projected to exceed $112 billion by 2030, growing at over 29% annually.

How Does Natural Language Processing Work?

NLP breaks down human language into components that computers can process:

Text Preprocessing

Before any analysis, raw text needs to be cleaned and structured:

Tokenization: Breaking text into individual words or subwords (tokens)
Lemmatization: Reducing words to their base form ("running" becomes "run")
Stop word removal: Filtering out common words like "the," "is," and "and" that add little meaning
Part-of-speech tagging: Identifying whether each word is a noun, verb, adjective, etc.

Language Understanding

NLP systems extract meaning from processed text through several techniques:

Named entity recognition (NER). Identifying and classifying entities like people, organizations, locations, and dates in text. For example, recognizing "Conbersa" as a company and "Neil Ruaro" as a person.

Sentiment analysis. Determining the emotional tone of text - positive, negative, or neutral. Used extensively in social media monitoring, review analysis, and brand reputation tracking.

Intent classification. Understanding what a user wants to accomplish with their text. When someone types "best CRM for startups," NLP identifies the intent as a product recommendation query for a specific audience.

Semantic understanding. Going beyond individual words to understand meaning in context. NLP models understand that "bank" means different things in "river bank" versus "bank account."

Language Generation

Modern NLP systems can also generate human-readable text:

Text generation. Large language models generate coherent paragraphs, articles, and responses. This is the technology behind ChatGPT, Claude, and other AI assistants.

Summarization. Condensing long documents into shorter summaries while preserving key information. AI search engines use this to create synthesized answers from multiple source pages.

Translation. Converting text between languages while maintaining meaning and context. Google Translate and DeepL use NLP models trained on billions of translated text pairs.

Why Does NLP Matter for Search and Content?

Search Engine Understanding

Google's major algorithm updates over the past decade have been NLP breakthroughs:

BERT (2019): Enabled Google to understand the context of words in queries. "Parking on a hill with no curb" was correctly understood as a different query than "curb parking on a hill" - something keyword matching alone could not distinguish.
MUM (2021): A model 1,000 times more powerful than BERT that understands information across languages and formats. MUM can process text, images, and video to understand complex queries.

These NLP advances mean that search engines understand content meaning, not just keywords. Writing for search now means writing clearly for humans - NLP models reward content that uses natural language, answers questions directly, and maintains topical relevance.

AI Search and Citation

Every AI search engine - ChatGPT, Perplexity, Google Gemini - relies on NLP to:

Parse user queries and understand what information is being requested
Evaluate retrieved content and determine which pages best answer the question
Extract key information from source pages to include in generated answers
Generate coherent responses that synthesize multiple sources into readable answers

Content optimized for NLP - clear structure, direct answers, specific terminology - performs better in AI search because the NLP models can more easily extract and cite it.

Content Analysis and Optimization

NLP tools help content creators optimize their work:

Topic modeling identifies related topics and subtopics you should cover for comprehensive content
Semantic analysis ensures your content covers the topic thoroughly, not just repeats keywords
Readability scoring evaluates whether content is accessible to your target audience
Competitor content analysis identifies gaps in existing content that you can fill

What Is the Relationship Between NLP and LLMs?

Large language models are the current state of the art in NLP. While traditional NLP used rule-based systems and smaller statistical models for specific tasks (just sentiment analysis, or just translation), LLMs handle virtually all NLP tasks through a single, general-purpose architecture - the transformer.

The key shift is from task-specific NLP models (one model per task) to general-purpose language models (one model for many tasks). This is why ChatGPT can translate, summarize, classify sentiment, extract entities, and generate text - all within the same model. Understanding this relationship helps marketers understand why structured, clearly written content performs well across all AI-powered systems - they all use the same fundamental NLP principles to process and evaluate text.