How Natural Language Processing will Affect the Future of SEO
In this article, we will dive in and discuss how natural language processing (NLP), and the integration of semantic web technologies with machine learning, may assist you in outsmarting your competition and obtaining a genuine SEO advantage.
We’ve been hearing more and more about AI in this past year, and what it can do for businesses, social networks, and large organizations in terms of improving their competitiveness. In this article, we’ll concentrate on how AI-powered SEO may be utilized to increase reader engagement and improve the discoverability of content.
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The integration of AI into search engines has enabled them to better understand the intent behind a searcher’s request. They can now detect synonyms, disambiguate queries never seen before by using named entity recognition software like sentiment analysis and natural language processing which focus on transforming words into machine-computable information for use in commercial Google searches or personal digital assistants alike.
Before we get started, let’s look back at the relevant search engine algorithm updates that have completely changed how search engines like Google display their results:
- The Panda Update was first introduced in February 2011 with the aim of reducing the visibility of low-quality websites in Google’s search results. The update was successful in achieving this goal and was specifically designed to greatly eliminate black-hat SEO strategies.
- The Hummingbird update was announced in September 2013 and marked a significant change to how Google processes queries. The update was designed to better deal with the complexities of modern search queries, which are often long-tail and contain multiple keywords. This update was in anticipation of the inevitable increase of mobile search queries.
- The RankBrain algorithm update was first announced by Google in October 2015. It is a machine-learning system that is used to help process and interpret search queries. The aim of the update was to further improve the quality of search results for users.
- The BERT algorithm update was announced by Google in October 2019 and is considered to be one of the biggest changes to the way Google processes search queries. The update is designed to help Google better understand the natural language used in search queries. BERT takes phrases entered into Google in conjunction with all of the other terms, rather than one at a time. The AI is then used to rank featured snippet results for both relevance and accuracy, allowing it to more accurately locate items for users. According to Google, the BERT model will have an impact on about 10% of searches in the United States.
So What exactly is Natural Language Processing?
NLP is a branch of artificial intelligence that deals with the interaction between computers and humans using natural language. NLP algorithms are used to process and interpret human language in order to derive meaning from it. The aim of NLP is to enable computers to understand human language in the same way that humans do.
In the past, search engines relied heavily on keyword matching to evaluate the relevance of a website for a specific query. However, with the aid of user intent understanding, search engines may now provide more relevant and accurate answers to a search query. NLP has several applications outside SEO, but one of the most important is its ability to assist search engines in better comprehending a user’s request and intent.
How Exactly Can Natural Language Processing help with SEO?
NLP can be used to help improve SEO in a number of ways. For example, NLP can be used to:
- Automatically generate meta tags: Meta tags are one of the most important elements of on-page SEO. They help search engines understand what your page is about and determine whether or not it is relevant to a user’s search query. NLP can be used to automatically generate meta tags by analyzing the content of a page and extracting the key topics.
- Analyze user intent: NLP can be used to analyze a user’s search query in order to better understand their intent. This can help you to provide more relevant and targeted results, which can improve the user experience and increase the chance of a conversion.
- Identify new keywords: NLP can be used to identify new keywords that you may not have thought of when using ‘lexical search.’ With machine learning and semantic search dominating the search engines, understanding semantic search becomes critical when choosing new keywords to target.
But Wait, What is Semantic Search?
Semantic search is a form of search that considers the meaning of a user’s query rather than just the keywords. Natural language processing (NLP) makes it possible for semantic search to exist. By recognizing the user’s objective, semantic search may provide more relevant and targeted results. This can improve the user experience and boost conversion rates.
How can Semantic Search help with SEO?
The benefits, while requiring more time and effort on the part of content teams, are significant.
- More keyword rankings in organic search.
- In the eyes of Google crawlers, improved content quality signals.
- And In the eyes of searchers, stronger brand authority and expertise, helping Google see your brand as its own entity with expertise in critical areas.
- Passage Ranking or People Also Ask format features.
- Increased possibilities for corporate backlinks. Instead of returning to search after visiting your website, users will stay there longer.
How can you get started using NLP and Semantic Search for your own SEO strategy?
The first and, in many cases, the most crucial impact of NLP on your SEO is that you must ensure that your web pages are structured in such a way that these algorithms can readily comprehend your content. The key to successful outcomes is for NLP engines to interpret language — whether we’re talking about spoken (voice search) or written language. Having proper Schema (structured data) implemented on your website can be critical to your position on the SERPs.
Internal linking and SEO content recommendation are the next two steps to implement properly. Internal linking and content recommendation tools are one way in which NLP is now influencing SEO. To see this in action, take a look at how The Guardian uses it in articles, where the names of individuals are linked to pages that contain all the information on the website related to them. Robert Weissgraeber, CTO of AX Semantics, notes that NLP boosts brand visibility with no additional effort by creating huge quantities of natural language content.
In recent years, the focus has shifted – at least for some SEO Experts – from keyword targeting to topic clusters. As a result, the next step we would advise you to take is Topic Targeting. The same outcomes may be presented to the user across multiple searches that share the same intent as search engines are increasingly capable of comprehending our environment and disambiguation becomes a factor; competition no longer focuses on selecting a particular keyword but rather on being relevant for a certain subject.
Finally, keep up with the latest trends in NLP and semantic search. This will help you to stay ahead of the competition and make sure that you’re using the best possible techniques for your SEO strategy.
Semantic search is the future of SEO, and with natural language processing only becoming more sophisticated, it’s important for SEO professionals to start learning about and implementing NLP into their strategies now. It can be tricky to stay on top of all the changes in this rapidly-evolving industry, but luckily we at Phrasing are always working hard to keep you updated on the latest news and trends. Stay tuned for more information on semantic search and how you can use it to your advantage!
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