Neither NLP nor SEO is new and unexplored territory. In a way, both deal with the intricacies of the linguistic properties of the human mind and its interaction with technology. In other words, both tend to try and make sense of how we humans express ourselves and return some output based on our linguistic input.
Search engines take user queries and attempt to make sense of them to understand the intent behind the query. And the optimization of content for search engines is, among other things, making sure the algorithms recognize your content as the correct answer to a user’s query.
But NLP has been around for far longer than SEO. Ever since the computer was invented, there was the problem of making it understand human language and complex semantics. Only recently have the two become interconnected. Google realized the potential breakthroughs in natural language processing have in providing users with the right content. The tech giant also realized that NLP is the key to staying afloat in the current technological climate.
This article will explain NLP fundamentals and how search algorithms use them. Moreover, it will cover the practical implications of natural language processing for content writers. Let’s dive into the broad subject that’s NLP and its relation to modern-day SEO.
A quick Google search asking “What is NLP?” can be quite misdirecting. Most results will offer mindfulness training courses, courtesy of various self-help gurus and neuro-linguistic programming. But if you Google natural language processing, you’ll get a far more impressive set of results. That’s the NLP we’re discussing here today.
Natural language processing is more than some pseudoscientific hosh posh loosely based on hypnotherapy. Unfortunately, there’s still no known way meditation can help with SEO, so if that’s what you’re here for, you’ll be sorely disappointed.
The kind of NLP we’re talking about is a science of processing and analyzing large amounts of natural language data. Natural language processing is linguistics, computer science, and artificial intelligence subfield. It’s concerned with programming computers to deal with the abundance of natural language data.
At first, NLP was used for machine translation of languages, but its uses soon exceeded simple translation. Today, NLP encompasses activities such as text and speech processing, syntactic and morphological analyses, and semantics. So, we could say NLP is an attempt to program machines to comprehend queries said or written in natural language, which entails understanding the intent behind what’s being said, too.
In other words, search engines are finally grasping the context of what’s being said, rather than just individual keywords and phrases. It should now be much more evident how potent NLP techniques are for a field that tries so hard to understand users as SEO does.
However, NLP is an overly broad subject for it to simply be integrated into another equally complex field like SEO. So how exactly does natural language search work, and how does it fit existing SEO practices?
In October 2019, Google announced a new update to its algorithm known as BERT. Once it fully rolled out three months later, BERT left a notable impact, affecting about 10% of all search queries.
Google’s core updates usually leave a lasting effect on how we approach SEO. They increase the volatility of page rankings for a short time until SEO experts and webmasters adjust to the latest changes. But the core update of January 2020 and the introduction of BERT were far more significant.
BERT, or Bidirectional Encoder Representations from Transformers, taps into NLP’s potential to empower Google’s search engine. The key term here is “bidirectional.” It’s what makes the latest core update to the search engine so powerful and almost human-like. BERT analyzes the query not just as a single piece of information but as a part of a greater whole.
SEO experts are used to extracting keywords and coming up with their variations. Instead of just focusing on keywords themselves, BERT looks at both the words that come before and after the keyword, hence “bidirectional.” Simple keyword stuffing, where you increase the density of a phrase in an article, is no longer effective, and BERT makes sure of that.
What that means is that BERT has the capability to derive the context of the query by analyzing it in its entirety. It considers the full content of the query. But that’s not the end of it — it’s also capable of learning the data after it evaluates the meaning from a context. Impressive, right?
And, since BERT is capable of understanding the meaning and sentiment of queries, it has a profound effect on featured snippets and their accuracy. With more context and understanding (courtesy of NLP), you should see more accurate and informative featured snippets.
If you Google NLP in relation to SEO, you won’t find a single article that doesn’t touch on BERT. That’s because the two are indeed inseparable.
BERT consists of two major components:
Data refers to pre-trained models in this case. They’re huge data sets for BERT to analyze using its processing methodology. Without the methodology, datasets are largely useless.
That’s where NLP comes in. It’s at the core of BERT, allowing it to work its magic. NLP is the engine driving BERT’s methodology.
Together, they have the power to reshape how we do and think about SEO. Why was the reshaping necessary, however?
Google’s search engine algorithms were already quite efficient as it is. They’ve gotten extremely accurate at recognizing keywords and phrases and understanding user queries.
We can all attest to how precise Google’s SERPs are and how rarely we have to stray from the first page to find what we’re looking for. It seems like Google has built a sufficiently large database of user queries and has enough data to predict what we want.
So, why BERT and why now?
The answer to that question lies in the next evolution of how people communicate with Google. And it is, indeed, a form of communication, as more and more users are performing voice searches.
What voice searches mean is that we are now querying Google using spoken language rather than strings of keywords. And, when we communicate using our everyday vernacular English, we tend to structure our questions differently than we would by using a search box.
What all this means is that there’s an increase in the number of long-tail keywords in use now. And, Google doesn’t have such an excellent grip on those as it does on shorter keywords and phrases. There are simply very few things in Google’s historical records to match the overbearing amount of long-tail keywords now in use.
Not only is there not enough data on long-tail queries, but it’s also highly inaccurate. That is because we, the users, aren’t so accurate with spoken language. We tend to describe and ascribe meanings of words while at the same time being very ambiguous. Natural language adds a lot of complexity because its irregularities and inconsistencies make it much more difficult to parse than other data.
Voice searches are revolutionizing how we use Google to find solutions to various problems. The search engines have to keep up with all the latest trends or risk losing valued customers because ultimately, it’s the user’s experience that keeps the lights on.
Google knows this. After all, its business model revolves around providing users with exactly what they want. Recognizing user intent and responding to it with correct content is at the core of what search engines do. However, they now faced a situation where it was questionable whether they could respond adequately or not. There simply isn’t enough historical data to anticipate the intent behind a query. So, changes had to be made.
That’s where NLP comes in! Google needed a way to understand spoken language and all the intricacies of context. And so, they made BERT. It’s Google’s way of staying on top of things and providing the search quality they’re so well-known for.
To sum up, it was long-tail keywords in voice searches that sparked the need for adjustment. Big data is still relevant and just as important as it always was. Keywords aren’t going anywhere, either. But now more than ever, it’s crucial to understand the context and sentiment of both the content and the query to match the two.
That’s a problem that the well-established field of NLP can successfully take care of.
NLP is here to help us organize all the world’s information at our disposal. It aims to achieve that by changing the way we understand queries as a whole. We used to do SEO by determining the keywords and then varying them enough throughout the text. With NLP in mind, it’s time to move from targeting keywords to targeting entire topics.
Using related keywords is not the only objective anymore. Instead, the focus should be on discovering and including semantically-related phrases. Thanks to NLP, we can make that semantic connection between different phrases. And that’s a crucial technological leap, given that more and more queries have an increasingly conversational tone.
It’s time to explain precisely how NLP understands the context and what qualities it introduces.
You might have noticed that we mentioned emotion and sentiment on several occasions. That’s because NLP is capable of judging the undertone of the content and ranking it on that basis.
The sentiment can be negative, neutral, or positive, based on the connotation of some of the words you used. If you tend to write positive adjectives such as “great” “advanced” “innovative” “creative” “useful” etc., you’re sending positive signals. It means you’re emphasizing the positive aspects of the thing you’re describing and indicates that the topic is discussed favorably.
Google now notices such signals and determines the sentiment of the content based on those signals. The same goes for any content that makes use of adjectives such as “poor” “underwhelming” “aggressive” and other words with a negative connotation.
Then you have nouns and pronouns, which usually bear no special meaning. They’re neutral in that regard and don’t influence the sentiment. But a combination of positive and negative signals can lead to a neutral sentiment mark for your content.
To determine sentiment, Google uses a scale that ranges from -1.0 to 1.0. The scale rates the sentiment of your article as follows:
The sentiment can have a notable impact on your web page’s ranking in SERPs – especially if it’s competing against positive content. Suppose Google rates the sentiment of your article as negative, and it’s facing positive content on page 1. In that case, it’s highly unlikely that Google will consider your page to be relevant and rank it higher.
Ask yourself what kind of emotions a piece of content has, compared to others that are ranking for that topic. Connect with your audience, convey emotion, and you’ll rank with NLP!
Entities are the next big change brought on by NLP, and they’re at the center of the update.
The entity is a noun or a pronoun (or even an entire phrase) that you can identify, classify, and categorize. Entities are how NLP will enable us to organize all the information on the internet. An entity can be anything, really. It can be a proper noun such as the name of a person or a kind of consumer goods. It can also be a location, a business, an event, and more.
The linguistic AI has never been better at named entity recognition and named entity disambiguation. It’s what drives all the advances in natural language programming and why Google has decided to implement BERT in the first place.
Ambiguity used to be a huge problem for search engines. People are well-equipped for dealing with ambiguous meanings daily — computers, less so. But now, with entities and other NLP tools, the search engine is able to establish a meaningful connection between different entities, satisfy the user’s search intents and present them with relevant results.
NLP employs two additional metrics to use entities even more efficiently: category and salience.
A category is a straightforward metric that deals with entities on a macro level. Truth be told, SEO experts are already aware of what categories are and use them on a daily basis.
In Google NLP, you’ll notice that category simply shows a generalization of what an entity is. It doesn’t necessarily have to be a broad category. Here are a couple of examples from Google’s Natural Language Processing API:
As you can see, categories consist of various subcategories to help the search engines better understand the content.
Salience, on the other hand, shows how relevant an entity is to the topic. It’s a measure of the importance of a single entity in relation to the text. The salience score ranges from 0.0 to 1.0. The higher the score – the more relevant the entity is to the subject of the page.
For example, in a blog post about web hosting, the word “server” will be far more relevant than “support.” Your goal is to ensure the most salient entities are tied to your keywords.
NLP relies on advanced syntactic analysis or parsing to draw the dictionary meaning from the text. The syntactic analysis places a great emphasis on writing punctually since NLP will use formal language to extract meaning.
But the syntactic analysis does more than just parse strings of symbols that conform to some formal grammar word. It also checks for the meaningfulness of the word – another trait of an intelligent linguistic AI.
So far, syntactic and entity analyses support the following languages:
Did you stop for a second to think about the purpose behind all of this? What Google’s trying to say with this NLP change to the algorithm is the same thing that it always highlighted: Write content for users, not search engines.
With NLP, writing for real people using natural language while avoiding jargon is more crucial than ever. Now that the search engine can dig deeper into the context and assess the sentiment of the text, it’s paramount to stay relevant to the topic.
Is this the end of keyword research? Not really. However, solely depending on the traditional keyword-based search for SEO is now a thing of the past. The more keywords you try to stuff, the more you water down the topic of the page. By focusing on real visitors, giving them content in real language, and being concise, you ensure both the reader and the machine will understand what you’re saying.
According to Google, there’s nothing that you should do to adapt to BERT — if you’re used to writing high-quality content, nothing will change for you. If you attempt to use black hat tactics, the search engine will pick up on it faster than ever and punish you for it. Keyword stuffing is now definitely a thing of the past.
But that doesn’t mean that you can’t use Google’s natural language processing API to improve your content further. Here’s what you can do to make the best out of your SEO efforts.
It goes without saying that when a link is positioned in the proper context, it gains a much higher value than if it were irrelevant. NLP and entity extraction algorithms can help you detect what important entities you might be missing in your content. By looking at a list of extracted entities, you can figure out what you missed to explain to your reader.
If you feel like it’s something your readers would be interested in, why not give it to them? It’s better than leaving them uninformed, at which point they’ll certainly leave your website to look for additional info on a knowledge graph or Wikipedia.
NLP is used to create internal links that matter to the reader – you can see what content your website is missing, provide it, and strengthen your internal linking game.
Semantic annotation in natural language processing creates enough data which allows you to predict what the user would like to read next.
Using semantic annotations and metadata, we can make better machine learning models to make such predictions and help users jump from one article to another. The more content you recommend, the longer the user’s dwell time on the website.
NLP can thus help you keep the user engaged and work wonders for your SEO!
The 301 errors don’t really have to be the end of the story for your website visitors.
Thanks to NLP’s recognition of synonyms and de-referencing, you can intercept users’ queries and redirect them to the correct page. This powerful mechanism allows a website to route the user to the right topic by intercepting all the alternative names a concept might have.
For example, by using de-referencing, you can intercept a user’s “NLP” query and redirect them to a “natural language processing” page instead.
NLP has the potential to revolutionize the way search engines understand content. It’s the next giant leap for SEO, and the experts who realize this now will be ahead of the curve tomorrow. Writing NLP-friendly content will help your site climb up the search rankings.
The best thing about it is that you should solely focus on your readers — use everyday conversational language, short sentences, and be punctual. Pay more attention to what your readers really want, and the search engines are sure to pick up on it. When all is said and done, the key to taking your SEO to the next level is simply being human and using natural language in your content.
After all, they are nearing the human levels of comprehension, so you might just as well treat them as such. Leverage the fascinating world of NLP to improve your SEO and boost user engagement!
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