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Natural Language Processing (NLP) in Search – Why is NLP Important for SEO?

Search Engines like Google and Bing are working hard to make the search more natural and relevant using conversational language to ease their users. This type of search is developed to understand the user’s intent which is referred to as Natural Language Processing (NLP) (also called as Natural Language Search).

What is Natural Language Processing?

Natural language is exactly what you think it is — it is simply the way we naturally speak. Advance Search engines like Google have accomplished full understanding of the intent behind the searchers, with the help of the continuous advancements of their linguistic AI capabilities.

Source: Wordlift.io

We have the future of the search (SEO) hinged on this.

Natural Language Search is carried out in regular language, phrasing questions as you would ask them if you were speaking to a person. These queries can be typed right into a search engine, spoken aloud with voice search, or posed as a question to a virtual assistant like Siri or Cortana.

Search technology has improved rapidly so that we can study search engines to understand longer, more complex questions with various components that can change one another and do not function or operate independently.


There are many techniques related to NLP. Search engines like Google and Bing, are no longer looking at keywords or phrases individually like it traditionally would. Now they are looking at phrases, sentences, paragraphs, and the query. They are looking at the sentiment of the searchers.

Now, more sophisticated algorithms can discern the emotions behind the statement or phrase. Strong feelings like sadness, anger, happiness, anxiety, negativity can be recognized. This is used widely in the field of marketing to discover the attitude towards different products, events, people, brands, etc.

NLP uses multiple techniques to reduce the time it takes to process data by removing and simplifying elements of sentences.

These underlying techniques are often used in higher-level NLP capabilities, such as–

Parsing - Parsing is all about splitting a sentence into its components to find its meaning. Now is easier for the algorithm to understand the relationship between the words through parsing.

Source: future-processing.com

Stemming - Stemming is a method of reducing the usage of processing power, thus shortening the analysis time.

Lemmatisation - Lemmatisation differs a bit from stemming in that it reduces words into their most basic forms.

Source: future-processing.com

Named Entity Recognition - Named Entity Recognition (NER) also called entity identification or entity extraction - is the process of matching named entities with pre-defined categories. With named entity recognition, you can now extract key information to understand what a text is about.

Source: monkeylearn.com

Stop Words Removal – It means to get rid of words that provide you with little semantic value. It, usually, removes prepositions & conjunctions, along with words like “is,” “to”, “my,” “I,” etc.

Source: onlinemediamaster.com


NLP adds a lot of benefits in terms of business values especially in the field of data science. With the help of NLP now computers can communicate with humans in their own language. Through NLP it is possible for computers to read text, hear speech, interpret it, measure sentiment, and determine which parts are important from a large volume of textual data.

With the advancement of technology today, machines can analyze more language-based data with consistency and objectivity.

NLP helps in making content understandable to the machines. Adding Semantic annotations in the content flow while publishing any content results in helping the natural language processing add a layer of semantically structured details that describes your content accurately.

Advance search engines like Google comprehend the meaning of the long tail words and phrases with the help of preceding & following content pieces.

With the latest advancements in understanding language, search engines are improving to understand queries with the help of machine learning.  NLP require SEOs and content creators to start thinking more about the technical constraint Google has in understanding how we write naturally.

There are a lot of practical ways you can implement NLP into your SEO.



  1. Structured Data Markup – These entities help to describe your content to search engines. An entity is a phrase or a word that represents an object which can be recognized, classified, and categorized.

Examples of objects are:

  • People
  • Consumer goods
  • Events
  • Numbers
  • Organizations

Google can distinguish these entities. The search engine can utilize this information to satisfy the user and provide better search results.

  1. Building internal linking and discovering content – internal links help visitors or users discover content from your website. They help search engines evaluate what the content is about. As well how effective the user experience can be for a visitor that arrives on a particular page of your website for the first time. A strong internal linking structure helps in SEO efforts significantly.
    NLP 6
    Source: neilpatel.com

  2. Content recommendations or suggestions – content suggestions helps to improve the dwell time. In SEO, dwell time denotes the time an user spends viewing a document after clicking a link on a search engine results page.
  3. Topic Targeting – In the past few years most of the SEO experts attention has moved from keyword targeting to clusters targeting. As search engines are now more capable of understanding the searchers sentiment and intent. The competition has shifted from specific keyword to relevance of specific topic.
    NLP 7
    Source: marketmuse.com

  4. Handling 404s & smart Redirections – Configure smart redirections and 301s by de-referencing entities and expanding the synonyms of a given topic, so that users can easily find the webpage they want to go to. You can see that it in action by directing your browser to a topic page like this:

    You will observe that the browser automatically redirects the request to a topic page for Search Engine Optimization located at this URL:


    NLP 8
    Source: wordlift.io


There are further advancements taking place in the ‘digital assistant’ field. Where we can see voice and natural language search tied together as digital assistants handle search queries. With the increasing popularity of virtual assistants like Siri natural language search is ready for prime time in the devices in your pockets. All these devices and virtual assistants are threats to Google’s dominance of search as their technologies make their way into our homes and hearts.

Source: Google


NLP is a complex concept. As much as 80% of the information that surrounds us is unstructured. Thus, NLP is one of the largest fields of data science. Continuous developments are being made in the field of NLP. We can expect it to see it effect our lives in more aspects.

Today, SEO expert needs to understand how NLP will impact search. What needs to be done to ensure that they continue to be effective? This rise of Natural Language Processing is the future for companies whose focus is on improving their search engine optimization.

If you've got any queries, please comment your questions below.

Until next time, Happy Marketing! 😊

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