Most people think SEO is all about keywords. It used to be. Not anymore.
Today, search engines guess what you mean, not just what you type. This shift began when Google launched BERT in 2019. Then came MUM in 2021, making search feel more human. These systems use Natural Language Processing to find what a user really wants, even when the search is vague or messy.
Website owners noticed real changes. Industry reports showed that sites using NLP in SEO started ranking more often in Featured Snippets and People Also Ask results. Many also gained 30 to 40 percent more long-tail traffic compared to older keyword-focused sites.
Why? Because NLP catches intent, not just words. It helps Google understand topics, emotions, and key entities the way a human reader would.
This guide shows how to use NLU, NLG, and Semantic Search to build content that ranks smarter. If you want real search growth, learning NLP is not optional. It is now the starting point.
What is NLP in SEO?
Natural Language Processing (NLP) is how computers learn to make sense of the way people talk. In SEO, it helps Google and others read your website like a real person would.
When Google launched BERT, it learned to read full sentences like a human does. Later, MUM took it further by connecting ideas across languages and topics. Both use Natural Language Processing to understand what people are really searching for, even if they say it in broken or roundabout ways.
That is where NLP in SEO steps in. It helps match your web page with real human intent, not just keywords. Think of it like this. If someone types, best shoes for running, they are not looking for a shoe dictionary. They want a clear, helpful suggestion.
Traditional SEO focused on exact keywords. It worked when Google was simpler. But now, it is all about Search Intent, context, and how concepts link together. NLP helps search engines understand the tone, structure, and meaning of content. Not just what it says, but what it means.
This shift also powers Semantic Search. That means Google connects related terms, entities, and emotions to figure out what result feels most helpful. So if your content reflects that intent, it ranks better.
Entity Recognition also plays a role here. It picks up names, brands, or locations that matter to the topic. Pages using clear, structured entities tend to earn more trust and show up in smart results like People Also Ask or knowledge panels.
What are the 4 Types of NLP?
Natural Language Processing sounds technical. But it just means teaching machines how people really talk.
Not textbook sentences. Not grammar rules. Real talk. Like what someone might say while multitasking on the metro or typing quickly with one thumb.
If SEO wants to connect with that, NLP needs to work in four different ways. You cannot guess meaning, write a good reply, handle casual speech, and clean messy text — with just one system. That is why NLU, NLG, core text processing, and NLI each play a part.
Let us break them down.
| Type | What it does | Where it helps |
| NLU | Understands meaning in user words | Voice search, search intent |
| NLG | Writes natural-sounding content | Snippets, FAQs, product blurbs |
| NLP Core Tasks | Cleans text for analysis | Tokenizing, stopword removal |
| NLI | Handles full conversations | Voice assistants, chatbots |
Natural Language Understanding (NLU)
Natural Language Understanding helps machines know what we really mean, even if we do not use perfect words.
It reads the full sentence, finds the need behind the words, and matches it to what solves the problem. This is how search intent is read properly.
After Google added BERT, it stopped scanning just for keyword matches. It began reading like a human.
Later, MUM made that smarter — now it can understand related questions across pages or even languages.
That is why most SEO experts today stop writing for bots. They write for what people are probably asking. NLU makes that possible.
Most SEO experts now focus on writing pages that match search intent, not just keywords.
Natural Language Generation (NLG)
Natural Language Generation is what writes the reply. Once the machine understands the question, this part builds the answer.
This is where things like featured snippets and auto-generated FAQs come in. You ask a chatbot, Where is my order? It answers, Your package is out for delivery. That is NLG.
Many websites use tools like T5 or GPT-based systems to create summaries, descriptions, and even titles.
This saves time and makes sure pages stay fresh without needing to rewrite everything by hand.
NLP Core Tasks
Before a machine can understand or reply, it needs clean input. That is where NLP Core Tasks step in.
First, it tokenizes the sentence. That means it breaks it into words. Then it removes common words like the and is — this is stopword removal.
Next, it finds the base form of each word using lemmatization. Like went, goes, and going all become go.
This step helps search engines focus on the real content and match it correctly to queries. If you skip this part, even good content can stay buried.
Natural Language Interaction (NLI)
People talk in bits. They change their minds mid-sentence. Add new info later. Machines need to follow all of that.
That is where Natural Language Interaction comes in. It links conversations together — like when you say, Show me hotels in New York, and then add, With free parking.
NLI connects both ideas. In search, this powers voice tools, FAQs, and People Also Ask blocks. If a system cannot follow the full thread, it gives broken answers.
That is why smart websites today plan their content like real talk, not just blogs. If it feels like conversation, it performs better.
How Does NLP Work?
Natural Language Processing works in steps. Each one brings machines closer to reading and understanding like a real person. From cleaning up messy text to adjusting models using live feedback, here is how it happens.
1. Text Preprocessing
Before any system can make sense of language, the text needs to be cleaned. This is where text preprocessing begins. The software first breaks everything into smaller parts using tokenization.
Then it removes extra filler like “a”, “the”, “and” — this is called stopword removal. Next, tools like stemming and lemmatization turn words like “buying” or “bought” into just “buy”.
This step is important because it clears the noise and shows what really matters in a sentence.
2. Feature Extraction
Once the text is clean, machines need to find the valuable parts. That is the job of feature extraction. Methods like TF-IDF help highlight important terms.
More advanced models use word embeddings like Word2Vec, GloVe, or BERT embeddings to map out how words relate.
These features give machines the context they need to understand what a sentence is trying to say — not just what it says on the surface.
3. Model Training
Now that the data is prepared, it is time to teach the model what to do. In this step, known as model training, the system looks at hundreds or thousands of examples to learn how to handle tasks like text classification, named entity recognition, or sentiment detection.
Tools like FastText, spaCy, or fine-tuned BERT are often used. During training, the model adjusts itself again and again until it can make accurate guesses on its own.
4. Evaluation and Feedback
Training is only the start. The model must now be tested. In this evaluation phase, experts check how well it performs using real-world queries. Common methods include precision, recall, and F1 scores.
In many teams, human reviewers step in as part of a human-in-the-loop (HITL) process. This means people help catch errors the model misses.
Based on what they find, the system is corrected and retrained again.
5. Fine-Tuning With Contextual Data
Even after testing, there is one more step — fine-tuning. The model is updated using live or domain-specific data. This includes search logs, FAQs, product pages, or user queries.
The goal is to make the model perform better in a specific field, like SEO, ecommerce, or healthcare.
This final layer helps build context-aware models, which can now understand not just words, but user goals and emotion too.
The 4 Pillars of NLP
Natural Language Processing works like a language detective. But not just one kind. It takes four lenses to fully decode how people speak, think, and mean things. Each pillar deals with a different layer of meaning. When combined, they give machines the power to read text like a person does — sometimes better.
1. Syntax
Syntax is the rulebook. It decides what makes a sentence correct. Where the subject sits, where the verb goes, how the words follow one another.
If the sentence is, The boy runs fast, syntax checks the word order and confirms it makes sense grammatically. But if you say, Run the boy fast, syntax might frown a bit.
Why does this matter? Because when NLP models break down your text for question answering or machine translation, they lean on syntax trees to figure out structure. No syntax, no order. Just word soup.
2. Semantics
Semantics is where meaning lives.
It does not care how pretty the sentence looks. It cares about what it says. For example, the words joyful and cheerful mean similar things. That’s semantic understanding in action.
In sentiment analysis, machines look at tone. Not just if the words are nice, but what they actually mean when grouped.
If someone says This product works, it’s neutral. But This product changed my life? That’s gold. Semantics caught the emotion.
3. Pragmatics
Pragmatics is the street-smart cousin of semantics. It knows people often say one thing but mean another.
Say someone asks, Can you shut the door? They are not checking your ability. They just want the door closed.
Pragmatics helps chatbots and voice assistants catch this drift — the actual intent, hidden beneath polite language or messy phrasing.
This pillar plays a huge role in search intent understanding. It helps Google guess what the user meant, even if the query looks vague.
4. Morphology
Morphology looks inside the words themselves. It sees roots, prefixes, and suffixes like puzzle pieces.
It’s unbelievable. Morphology breaks it into un- + believe + -able. Now the model knows it means “not easy to believe”.
This helps in lemmatization, stemming, and other text normalization tasks. Without morphology, words like jumping, jumped, and jumps would be treated like strangers. But with it, they all return to their root: jump.
How to Optimize SEO Content Using NLP
Great SEO content is not about stuffing keywords anymore. It is about knowing what people really mean when they search. That is where Natural Language Processing (NLP) steps in.
NLP connects the dots – topic relevance, search intent, emotional tone, and structure. All at once. It is not magic. It is a method. And you do not need to be technical to use it.
This is not about writing more. It is about writing with meaning. Every word should help the reader and help the page rank. Let us break it down step by step.
1. Find the Right Topics With Semantic Modeling
Semantic Topic Modeling helps you find what topics go well together. Instead of guessing keywords, it shows how people connect ideas when they search or read.
When you write a blog, the first step is picking the right theme. But not just any theme. You want the one that matches what people are really searching for — not just the word they type, but the meaning they want. That is where semantic models help.
These models group topics using machine learning. They check which entities often appear together in real articles, not just headlines. For example, if you write about “healthy skin,” the model may also pull in related topics like “hydration,” “SPF,” and “vitamin C.”
How to do it step by step:
- Start with existing articles that perform well. Pick 5–10 from your site.
- Run them through Google NLP API or TextRazor.
- Check which entities appear most often. Look at nouns and names — not filler words.
- Group related entities using those outputs. This is your semantic cluster.
- Build your new article around that cluster — not just one keyword.
When you do this, your content becomes more complete. Search engines understand it better because you have used entity linking, topic modeling, and context matching — all part of Natural Language Processing.
This helps your content show up not just in keyword searches, but also in People Also Ask, related topics, and voice search results.
2. Improve Content With Entity Density
Search engines no longer look at just keywords. They check for entities. These are names of things — people, places, tools, ideas, brands. Entity Density means how often these key things show up in your content, without overdoing it.
If your article talks about electric cars, but never mentions “battery,” “charging station,” or “range,” the page may feel weak to Google. Because it is missing the full topic.
Now, this does not mean repeating one word again and again. That hurts your ranking. The trick is to cover all the important related terms in a natural way.
Here is how to improve your entity density:
- Run your draft through Google NLP API or TextRazor
- Look at the entity salience score — this shows which topics stand out in your text
- Check what is missing — are core terms like “price,” “types,” or “benefits” left out?
- Add these terms where they fit — inside subheadings, bullet lists, image alt texts, etc.
- Keep flow natural. Do not copy-paste terms in a robotic way. Rewrite with meaning.
Example logic (no place or product names used):
If someone searches for “how to choose the best running shoes,” they probably care about comfort, grip, weight, and foot type. So, your content should not just say “running shoes” many times. It should include entities like cushioning, ankle support, sole type, and arch.
That is how you show relevance — not by stuffing, but by covering the complete context using NLP-backed terms.
This process builds semantic strength. It improves topical authority, and helps you rank higher for long-tail searches, featured answers, and even voice results.
3. Adjust Tone With Sentiment Signals
Words carry weight. Some feel sharp. Some feel soft. Some build trust. Others turn people away. That is why adjusting the tone of your content matters more than ever.
You are not just writing for keywords. You are writing for moods. Search engines now understand whether your tone feels helpful, angry, neutral, excited, or sad. This happens through Sentiment Analysis. It checks the emotional signal behind your words.
Say you are writing about loans. Cold, robotic phrases like “disbursement eligibility verification” might hurt trust. But if your words sound calm, supportive, and clear, it invites readers to stay. Search engines pick up on that tone and reward it when users engage longer.
So how do you fix tone problems?
Start by reading your page aloud. Does it feel warm or cold? Friendly or stiff? Rewrite harsh phrases into simpler, real-life words. Use emotion-rich verbs. Replace vague nouns with exact ones. Keep your reader in mind at every line.
When your content speaks with the right emotion, it speaks to both people and search engines.
4. Optimize for Featured Snippets
Featured Snippets are those quick answers you see right at the top of Google. They get clicked more than any other result. If your content fits into that box, you win big without ranking #1.
Getting there starts with one thing—clarity.
Search engines pick snippets that answer questions fast. Not fancy. Not long. Just clean, helpful answers that match how people ask things.
To do it right:
- Pick one question per section. Ask yourself: what will someone Google to reach this page?
- Start your answer right after the heading. No intro, no buildup.
- Use bold N-grams or common query phrases naturally. Not forced.
- Write answers in 40 to 50 words. One paragraph or a short list.
- Use simple formatting: bullets, tables, or steps. These help Google pull content directly.
Let us say you are writing about “how to clean glass windows.” Do not start with history or tools. Start with the step-by-step answer. Make each point sharp and clear.
Remember, NLP helps you here too. If your structure fits the search intent, and your language matches common phrasing, your chance of being picked goes way up. Especially in People Also Ask boxes and voice search results.
5. Link Your Content to the Knowledge Graph
The Knowledge Graph is Google’s big brain. It connects people, places, and things through relationships. If your content fits inside that map, it gets more trust. That means better visibility.
Most websites ignore this. They write random facts with no clear context. But if Google sees your page as part of its entity network, it can rank you for more than just your target keyword.
Here is how to build that connection:
- Focus on entities, not just keywords. These are things like product names, people, organizations, and locations.
- Use those entities consistently in headings, text, alt text, and metadata.
- Make sure your entities link naturally to each other. For example, do not just mention “electric cars.” Mention Tesla, charging stations, battery range.
- Use schema markup to help machines understand what each entity means. If you mention a book, mark it up as a Book, not just plain text.
Linking to the Knowledge Graph is not about stuffing names. It is about building context. The stronger your entity relationships, the easier it is for search engines to place you inside the graph.
When that happens, your content becomes part of the conversation—not just a lonely page on the web.
6. Use NLP Tools to Test and Improve Content
Writing the page is not enough. Testing your content is what turns it into a real ranking asset. That is where NLP tools give you a real edge. They do not check just spelling or grammar. They read your page the way a search engine would.
Start by using tools like Google Cloud Natural Language API or TextRazor. These tools scan your content for entities, sentiment, and topic coverage. They highlight what your text really talks about — not just what you think it says.
Follow this simple step-by-step:
- Paste your draft into Google NLP or TextRazor.
- Look at the entities it finds. Are they clear and on-topic?
- Check entity salience. This shows what your main focus is. If it feels scattered, you need to fix that.
- Review the sentiment tone. Is it neutral, positive, or off-balance for your topic?
- Compare your results with a top competitor’s page. Spot what they included that you missed.
If your article talks about skin care but skips related terms like “SPF,” “vitamin C,” or “hydration,” these tools will catch it. That gap hurts your ranking.
The best content is not just written — it is tuned. NLP testing makes sure your article sounds right, ranks well, and hits all the signals that Google actually tracks.
How to Use NLP in SEO for Better Search Rankings
Search engines no longer match just words. They match search intent, emotional tone, and topical coverage. Your content either clicks with that or it vanishes without a trace.
If your page talks around a topic but never touches its core entities, Google may not trust it. But when you embed semantic context, entity salience, and clear intent signals, things change. Your content ranks faster. It stays longer. And it reaches the right audience.
Let us break down how to use it—step by step.
1. Tune Content With Entity Recognition
If your content skips key entities, Google skips your page. Simple as that. Today’s search engines use entity recognition, not just keyword matching. They spot names, brands, tools, products, topics, and link them to real-world concepts through the Knowledge Graph.
Here is what works
write your content, then test it using entity salience. If your article talks about electric cars, but Google only spots “car” as the main entity, your content feels weak.
That means the topic is not focused. Or the structure is too broad.
How to fine-tune it:
- Use a tool like TextRazor or Google NLP API.
- Paste your content. Review the entities it detects.
- Look at the salience scores. These show which topics dominate.
- If your core topic ranks low, tighten the structure. Add relevant supporting entities.
- Rewrite key sections using stronger semantic connections.
Do not overdo it. This is not about stuffing more words. This is about refining meaning.
When your content reflects strong entity presence—along with proper structure and intent matching—Google understands it better. And that leads to higher rankings, especially for long-tail and zero-click searches.
2. Predict User Behavior Using NLP Sentiment Models
Search engines track more than clicks. They measure how readers feel while reading. If users bounce, scroll too fast, or drop off early, your rankings suffer—even if your keywords are perfect. That is why using sentiment analysis matters.
Sentiment models spot whether your tone is helpful, harsh, neutral, or unclear. Google quietly factors this into engagement signals. If your page feels confusing or cold, users leave fast. And that tells the algorithm your content missed the mark.
Here is how to apply sentiment NLP:
- Run your page through a tool like Google Cloud NLP.
- Check if the sentiment score fits the topic. Positive, neutral, or negative? Depends on the subject.
- Rewrite weak sections. Use emotion-rich words where needed.
- Match the reader’s emotional state. Calm for finance. Uplifting for health. Neutral for technical.
Keep this balance natural. Do not try to sound cheerful where the topic needs seriousness. The goal is to mirror the user’s mindset. When your content speaks their emotional language, they stay longer. They trust more. They convert faster.
Use NLP sentiment models to spot this early—before the bounce rate hurts your rankings.
3. Use RLHF to Optimize Dynamic Pages
Most SEO content stops at the page level. But some pages keep changing—like landing pages, ecommerce filters, FAQ pop-ups. These need more than static keywords. This is where Reinforcement Learning from Human Feedback (RLHF) comes in.
RLHF lets your system learn from what real users do. Not what you think they want. What they actually click, ignore, scroll, expand. It tracks real behavior over time and updates your content layout or answer order based on that.
Here is how to start using RLHF for SEO:
- Track actions on dynamic content blocks: What answers get expanded? What filters get ignored?
- Set basic reward functions: Did they stay longer? Did they scroll more?
- Use these rewards to train content tweaks: Headline positions, featured blurbs, or CTA tone.
- Repeat the loop weekly. More user feedback, better results.
This is not just AI hype. It is what powers smart search apps and personalized SERP results. You can apply it even with simple tools. What matters is the loop. Learn. Test. Improve. Then repeat.
Dynamic SEO is no longer about writing once. It is about writing, watching, and reacting. RLHF makes that loop smart and SEO-ready.
4. Align Content With Semantic Search
Search engines now care more about meaning than exact words. That shift is called Semantic Search. It links what people say to what they really want. Your content must do the same.
If someone searches for “easy ways to save energy,” they might not type “energy audit” or “smart thermostat.” But if your page includes those ideas naturally, it gets picked. That is how semantic search works.
Here is how to align with it:
- Write around topics, not just keywords. Cover every angle people care about.
- Use entity clustering: add related concepts like product types, benefits, and how-tos.
- Connect your sections with soft bridges: show relationships, not just facts.
- Embed NLP terms naturally. Words like Search Intent, Entity Recognition, Lexical Chains, and Concept Mapping help Google map your content better.
Try this structure:
- One core idea per section.
- A cluster of related terms woven in — not dumped in.
- Clear transitions that mimic real conversations.
Good semantic structure makes your content feel complete. It feels helpful. Search engines notice that. And they reward it with higher ranking and snippet picks.
Common Mistakes When Using NLP for SEO
Big SEO wins often hide behind small fixes. And in NLP-driven content, a few wrong steps can quietly kill your rankings. These are the blunders to avoid if you want NLP to help — not hurt — your site.
Focusing Too Much on AI, Ignoring Human Readability
Chasing perfect NLP scores? Stop. Users do not read TF-IDF outputs or salience charts. They read words. And if your content sounds robotic, it fails — no matter how optimized it looks on paper.
Fix: Always write for humans first. Then test for NLP structure.
Overstuffing Semantic Entities
Yes, Google wants to see topical depth. But stuffing every possible entity into a page makes it unreadable. This confuses both the algorithm and your audience.
Fix: Use entity salience to guide importance. One strong topic beats 12 weak ones.
Ignoring Discourse and Pragmatics
You covered keywords. You wrote clean paragraphs. But something still feels… off. That is a discourse failure. The flow of conversation is broken. The context does not breathe. You skipped the human rhythm.
Fix: Reread aloud. If it sounds strange or flat, restructure for pragmatic SEO. That means sentence variety, transitions, emotion cues, and mental pauses.
No Feedback Loop or Re-Training
Published a page once and moved on? That works for books. Not content. NLP performance changes with trends, algorithm updates, and even user mood shifts.
Fix: Recheck entity relevance and sentiment tone quarterly. Adjust based on NLP tools like Google NLP API. Learn. Tweak. Repeat.
If your content sounds like it was made to pass a test, you already failed the reader. NLP is not a checklist. It is a communication bridge — use it to sound smarter, not synthetic.
Mistakes in NLP-driven SEO are usually not technical. They are emotional. Flow, feel, and focus matter more than formulas. When in doubt, choose clarity. Then layer in structure. That balance wins every time.
What Are the 5 Keys to Anchoring NLP?
Anchoring is not some magic marketing trick. It is basic human memory. You feel something once, it fades. You feel it again, in the same way, it sticks. NLP uses that natural pattern to build trust and action into content — quietly, effectively, permanently.
Let us break it down.
1. Unique Trigger Creation
Not every link or phrase pulls attention. The brain needs a spark. That “spark” is your unique trigger. Maybe it is a word. Maybe a specific question.
Use it inside internal links. Tie it to an emotion. Let it appear where action happens.
Example logic: “Need it done fast?” → Used again in links: “Fast help for passport renewal”.
NLP Term Used: Anchoring, Trigger Cues
2. Intense Emotional State Capture
You have 3 seconds. That is it. If your headline or opening does not make someone feel something, they leave. So write like you are talking to a friend in a rush.
Hit a feeling: fear, relief, curiosity, pride. That is the only time anchors form — when emotion is real.
Tip: Scroll-stoppers work better than clever words.
NLP Term Used: Emotional Capture, Affective Triggering
3. Anchoring Through Repetition
Humans remember what they see often — but not if it sounds robotic. So repeat your theme. Your message. Your tone. Not word-for-word, but rhythm-for-rhythm.
The idea should echo softly from intro to CTA.
Do not repeat phrases. Repeat impact.
NLP Term Used: Repetition Anchoring, Semantic Reinforcement
4. Contextual Consistency
Ever clicked a Google link, read the title, and felt confused by the page? That kills trust. NLP anchors need matching tone and meaning from meta title to body text. Same topic, same feel, same framing.
The user should never feel a shift in voice.
NLP Term Used: Contextual Flow, Framing Consistency
5. Future Reinforcement of Anchor
The anchor does not end on the page. It comes back later — in your next post, in a CTA, maybe in a retargeting message.
That moment of “Hey, I remember this!” builds familiarity. Familiarity builds clicks.
You planted the seed. Now water it later.
NLP Term Used: Long-Term Anchoring, Memory Repetition
These 5 keys are not about tricks. They are about memory. Emotion. Rhythm. SEO is no longer just metadata and links. It is human recall — built on anchoring, reinforced through language, delivered via NLP.
How to Create an NLP Anchor?
People do not always click because they need to — they click because something grabs them. That “grab” is the anchor. A feeling. A question. A word that hits just right. That is what NLP anchoring builds inside content. You guide attention, memory, and even action — without yelling.
Let us walk through it step by step.
1. Spot the Emotional Trigger
First, you need to know what moves the user. Not what they search — what they feel when they search. Are they confused? Tired? Curious? Worried?
If you are writing about “passport delays,” the emotion might be panic. For “eco-friendly roofing,” it might be hope or pride. That emotion becomes your root signal.
NLP Term Used: Emotional Anchoring
2. Create a Sensory Hook
Now turn that emotion into something sticky. Use sensory-rich phrases, visual words, or question hooks. For panic, it could be: “Still stuck without your passport?” For hope: “What if your roof could save the planet?”
These are not just intros. They set memory cues.
NLP Term Used: NLP Triggers, Lexical Framing
3. Repeat Across Key Spots
Once you have the phrase or tone, plant it in 3 main places:
- Meta Title — makes the user stop scrolling.
- H1 Tag — confirms they are in the right place.
- Subheading — gently reactivates the emotional signal mid-scroll.
This does not mean repeating the exact line. It means echoing the emotion. The tone. The intent.
4. Reinforce Through Flow
Anchor only works if you do not break it. So build your CTAs, next-section links, and even bullet points to match the tone of your original emotional hook.
Let us say your piece started with “Worried about passport delays?” and you answered it well. Your CTA can softly continue that loop: “Talk to a passport expert now. No waiting.”
Now that signal loops and closes.
NLP anchors are not tricks. They are human cues wrapped inside language structure. When done well, they increase dwell time, scroll depth, and click-throughs — quietly, effectively, emotionally.
How to Use NLP in SEO Quickly
To use NLP in SEO quickly, follow these 5 steps:
- Pick a keyword that matches what users mean, not just what they type
- Scan top pages with Google NLP API to find top entities
- Add those entities naturally inside your headings and paragraphs
- Rewrite cold or robotic lines to sound simple and human
- Run one final check with TextRazor or Google NLP to test salience
This makes your page match real user intent, boost entity coverage, and win snippet spots.
