How Natural Language Processing (NLP) Is Shifting the Way We Do SEO

Websites today are reliant on search algorithms to get them onto the coveted first page of search engine results. Developments like voice search and smart assistants are ushering in a generation of users looking for very specific information on the internet. Search engines have to keep up with changing search habits and also ensure that they show relevant results.

The first step towards providing relevant content in the search results is an understanding of the question. Computers use a complex system called Natural Language Processing (NLP) to analyze text so they can understand the relevancy. NLP allows algorithms to sift through millions of pieces of online content and provide the best answer to users for specific queries.

Here is where search engine optimization comes into play. SEO allows you to create content that will be boosted by Google’s algorithm to the top of the results.

So, how does NLP affect your site’s SEO, and why should it matter to marketers? This guide will look at the relationship between NLP and SEO and how Google’s BERT algorithm has been a game-changer in online searches.

• 1. What Is Natural Language Processing?
• 2. BERT and NLP Go Together
• 3. The Google SMITH Update
• 4. What's Google's Perspective on NLP?
• 5. NLP and Content: a Relationship Worth Knowing
• 6. NLP in SEO: what We Can Expect in Coming Years?
• The Bottom Line

1. What Is Natural Language Processing?

NLP allows a computer to understand and break down human language, both written and spoken. In some cases, it also predicts behavior. For example, when you run a search query, and Google offers suggestions based on your previous searches, you are witnessing NLP in action.

Platforms built on NLP allow users to have a conversation with a computer using human language. When you say ‘Hi Siri,’ your iOS device listens to the voice command that follows, then the smart assistant initiates an action based on its interpretation of your command.

Even something as seemingly simple as autocorrect is also a form of NLP, as your phone is ‘trained’ to recognize the language you use and suggest the appropriate words for what it thinks you’re trying to say. Natural language processing (NLP) also makes tools such as voice assistants, spam filters, and autocorrect possible.

NLP uses machine learning, a branch of artificial intelligence, to analyze chunks of information. For example, a blog post. An algorithm uses a statistical modeling engine to analyze your search query and consider the best result.

When analyzing an article, an algorithm classifies individual words into nouns, verbs, adjectives, prepositions, and so on. The algorithm then counts the number of times words are used. It then uses that information to ‘understand’ the text. It looks at all these entities and analyzes how they are connected. The algorithm will use this data to understand how the entity is related to the content topic.

A few seconds is all it takes for an NLP engine to take your input, understand it, and churn out a relevant response. Whether you’re using Siri, Alexa, or another NLP platform, one thing is certain: NLP is here to stay. If you want your website to stay relevant, you need to understand the basics of NLP and how companies like Google use this technology.

2. BERT and NLP Go Together

Over time, search algorithms using NLP have become more advanced. Simple keyword stuffing, where you increase the density of a phrase in an article, is no longer effective.

The Bidirectional Encoder Representations from Transformers (BERT) is an open-source machine learning framework for NLP introduced by Google in 2018. The BERT language model is unique in its ability to process ambiguities in language and possesses a human-like ‘common sense.’

BERT is built on English Wikipedia and the Brown Corpus, a major electronic collection of text samples that detail everyday use of American English, totaling over a million words. Besides depending on its already extensive knowledge base, BERT uses Google search data to learn how people communicate and ask questions. This way, it can recognize search queries, translate them more accurately, and offer better answers.

Fundamentally, the BERT algorithm provides Google with a better understanding of a text. The BERT algorithm improves the way Google analyzes the interactions between words and phrases.

In the example above, Google illustrates how BERT has changed how Google serves up search results to reflect user intent more accurately. Before the BERT update, searching for ‘Brazil traveler to USA need a visa’ pulled up an article from the Washington Post. While the WaPo is a credible news source, it doesn’t contain all the information a Brazilian traveler to the US will need on acquiring a visa. In fact, the article is targeted towards Americans who are planning to visit Brazil.

The 2019 BERT update means that the U.S. Embassy in Brazil’s page appears as the first result for this search. NLP allows the search engine to correctly understand the placement of the words in the search phrase and responds with the appropriate top result.

Google explains that the update makes better sense of sentence fragments and can figure out the context of a search more accurately, resulting in better search results. Pretty clever 😉

3. The Google SMITH Update

In 2020, SEO experts started to discuss Google’s new SMITH algorithm. The SMITH algorithm improves on the BERT algorithm. The BERT helps improve the understanding of how words interact with each other to gain a sense of the meaning of chunks of text. The SMITH algorithm helps Google understand the meaning of passages of text within the context of an article.

Google could be using the SMITH algorithm to pull out instant answers from articles. These are answers that are delivered through the search results to people using the search engine. The screenshot above is an example of instant answers.

4. What's Google's Perspective on NLP?

While BERT represents a huge step forward for Google, it affects only about 10% of all searches. Google has also poured many resources into its other NLP-related projects that aim to create better machine-learning algorithms that can provide the best answers and even hold a conversation.

Google uses NLP across various products, including its search engine, translator, and mobile platforms. The company focuses on using NLP to create consumer-centric statistical methods that power its machine learning models.

For Google, NLP is the key to building a superior search engine that offers the best answers to any question. Users today know exactly how to use Google to find what they are looking for, which means that they tend to use more long-tailed keywords and unique searches.

In the past, Google said that about 15% of Google searches were first-time queries, which meant that there wasn't enough historical data for the search engine to fall back on. While the amount of historical data that Google collects is constantly rising, the only way a search can provide specific answers is to understand better language as it evolves. NLP is the only way for Google to stay ahead in its industry.

5. NLP and Content: a Relationship Worth Knowing

The ability to get your content organically ranking on the front page of Google for terms that are relevant to your business will help your firm make more money.

As Google makes changes to their search algorithms, SEO agencies, SEO practitioners, and SEO tools react and adapt to the changes. It’s a constant battle, with Google trying to ensure their search results are not manipulated and companies looking to get their content to rank.

One of the most recent developments in SEO is the release of SEO tools that use NLP to suggest what words and phrases, and in what quantity, you should include in an article if you want that content to rank for a certain phrase. Below is a simple description of how these tools work:

• Analyze the word density of articles ranking on the first page of Google
• Identify commonalities between ranking articles. For example, the headings that are used, or the frequency of keywords and phrases
• Provide you with a list of phrases to include in an article and how often you should include these phrases
• Provide you with a list of key questions you should answer based on the results that are appearing on the first page of Google

As a writer, the insights you gain through these tools can be invaluable. They help you create better briefs, provide insights into search intent, and help you create content that is most likely to rank on Google. In short term, this battle is likely to result in increasingly similar content appearing in search results as people imitate content already ranking on Google.

6. NLP in SEO: what We Can Expect in Coming Years?

The ability to understand the text and analyze its context has implications for SEO practitioners everywhere. They will need to consider NLP-backed algorithms if they are to help their clients climb up the search engine rankings.

While backlinking and keywords are still important, your content needs to be relevant. Keyword stuffing has been ineffective and frowned upon for some time now, and it will become even more irrelevant as Google rolls out ever more accurate NLP-based ranking models.

You need to take another look at your keyword research strategy and how you use keywords on your site. We’ve seen that Google is looking at the relationship between entities and the article topic.

The importance of SEO tools that analyze text and use NLP algorithms to predict the type of content you need to create by Google reverse engineering will grow. These are trends you need to keep up with if you want to create content that will appear on the front page of Google.

The Bottom Line

NLP helps us to have conversations with computers, search engines, and apps to find the information we want more easily. Just like a person learning a language, a natural language processing platform keeps learning and improving its comprehension of the language so that it can give better answers to user questions. Google’s BERT update was a major leap forward for NLP as it allowed Google to provide more accurate search results by putting online content into context.

Since NLP’s major goal is to understand human language better, this also means that marketers need to consider it as they create and promote content. Because NLP-based search engines look for word combinations that match user intent, marketing content needs to be more precise and targeted, especially now that users are being more and more specific in their searches.

Understanding how NLP sifts through billions of lines of information will help you create content that is relevant and actionable. Writing NLP-friendly content, in turn, will help your site climb up the search rankings.