🤖The Power of NLP in Search: Enhancing SEO and User Experience

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Search typos are annoying but search engines have learned very well to go past it.

The ability to correct typos in search queries is made possible by a sophisticated combination of machine learning models. These models work in harmony to deliver the most accurate search results possible.

Fortunately, these models are extensively trained to handle various scenarios, including instances where users mistype complex names or terms.

Let’s take one of my favourite actors and challenge your spelling skills. I bet many of you have struggled to type Matthew McConaughey correctly on the Google search bar.

The reason search engines consistently provide the closest results to your typo-ridden queries can be attributed to the combination of Natural Language Processing (NLP) algorithms.

In this post, I will explain the significance of NLP in Google Search and how this can be applied to optimize our SEO endeavors.

What is NLP in SEO

People on the Web always want some kind of definition. So let’s start off with one.

Natural Language Processing (NLP) is a field of computer science that focuses on the interaction between computers and human languages.

Google Research Area

NLP has a wide range of applications, including machine translation, speech recognition, and text analysis.

From here, you can guess there may be potential implications in the context of SEO.

In fact, NLP is essential for optimizing website content to align with how people naturally search and ensuring search engines correctly interpret the content. This involves applying NLP principles in entity-based research, content creation, and on-page optimization to improve the visibility, relevance, and user experience of a website in search engine results.

What is important to note is NLP is being leveraged consistently in Google’s ranking process since the crawl cycle. Using such a semantic machine learning model, Google can easily process webpage content and match it with user queries before passing it on to indexation.

From a technical standpoint, NLP utilizes techniques like tokenization.

Tokenization is a data security term that refers to the process by which sensitive data, such as an unstructured text corpus, is replaced with tokens and hence broken down into their linguistic stem.

This process is relevant for normalizing keywords into recognizable entities to uncover the underlying semantic meaning that will be used to define a topic cluster strategy.

The Importance of NLP in SEO

When it comes to searching for information on the web, we are familiar with the traditional keyword-based search approach. This method involves entering specific words or phrases into a search engine, hoping to find relevant results.

With the advancements in Natural Language Processing (NLP), a new paradigm of search has emerged. NLP-based search aims to understand the meaning and context behind search queries, providing more accurate and personalized results.

By leveraging NLP techniques, search engines can comprehend the intent behind user queries, consider synonyms, interpret natural language patterns, and deliver results that align with user expectations.

🔦 You can be an NLP yourself!
In this blog post you can visualize the search intent flow behind your website’s queries

This shift towards NLP-based search opens up new possibilities for a more intuitive and user-friendly search experience, where users can express their queries in a more natural and conversational manner.

And yes, I think you should know the drill by now.

NLP bridges the gap between user search behavior and search engine processing methods.

By utilizing NLP techniques, search engines understand the context, intent, and meaning behind search queries, delivering personalized and relevant search results.

This allows for the creation of engaging and user-friendly content that is easily parsed and understood by search engines.

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How is NLP Used in Google Search?

Google uses NLP in a number of ways to improve its search results.

For example, NLP is used to:

• Identify entities in search queries. For example, if a user searches for “the president of the united states,” NLP can identify the entities “president” and “United States“
• Understand the intent of search queries. For example, if a user searches for “how to make a cake,” NLP can understand that the user is looking for instructions on how to bake a cake irrespective of the incorrect use of the verb (make instead of bake).
• Personalize search results. For example, if a user has previously searched for information about cars, NLP can use this information to show the user more relevant search results when they search for the entity “car.”

The latest release of the search generative experience (SGE) in Google Search is going to bring a wave of NLP-based features. Let’s explore a few:

• Contribution to the AI-snapshot generation. At the time of writing, SGE is currently a beta feature promoted by Google corporate in the U.S. vertical, and it appears to resemble an enhanced version of “Featured Snippets.” Featured snippets are concise summaries of web pages that are displayed at the top of search results. NLP is employed to identify the most pertinent text from a web page and generate a featured snippet.
  
• Answer questions. Given the conversational nature of SGE (Search Generating Entity), users can expect to receive comprehensive answers directly from the chat prompt, without needing to click on a webpage. NLP is employed to understand the meaning of the question, allowing the generative AI to calculate the probability and generate the best answer based on its pre-trained database.

How Can l Use NLP on my Website?

There are various methods you could use to encapsulate NLP within the content strategy for your website.

The best use case of NLP I advocate is building entity-based schema markup for your website. The process involves using a dedicated NLP tool to extract the major entities from the content of a webpage and wrap up the most salient entities in schema properties like @mentions or @about.

🔦 Take inspiration for your content strategy using topic modelling

Now let’s explore the different tools you can utilize NLP for on your website.

Despite the common recommendation to use the official Google NLP API, it is important to note that this API is just a demo and is trained on outdated and small datasets.

This API has a good metric called Salience which measures the significance (i.e the “weight”) of entities in your text, with a higher value indicating greater relevance and vice versa.

However, it’s worth noting that salience is not necessarily the most critical metric as it can be prone to errors due to the small size of the pre-trained dataset used to generate the output.

For better results, I personally recommend exploring advanced and free NLP tools such as Dandelion, Text Razor or spaCy.These tools can be trained with new texts and offer improved performance.

If you aren’t really a nerfed coder, I recommend using Text Razor instead of the other services since they would require you to adopt their API within a coding environment.

Conclusion

If you are looking for more inspiration, here are some ideas to level up your NLP game in SEO with Python:

🦊 Identify entities and audit sentiment using Python on landing or product pages

🦊 Conduct entity research powered by NLP using tokenization techniques

🦊 Run an Entity-based competitor analysis with Google NLP API

FAQ

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