What Is a Natural Language Search?

Search engines are using increasingly advanced tech and developments to provide better results for users. Yext introduces Natural Language Search and describes how it works.

min read

Way back in the year 1990, on September 10th of that year, Archie, the first search engine was released into the world by Alan Emtage, Bill Heelan, and Peter Deutsch.

It was crude, but it worked. Allowing users to index and then recall files, Archie was the prototype for today's search engines.

But the search engines we use now are far beyond what Archie could imagine back in the 90's.

Today, users are used to and expect high-speed results that are accurate and personalized to their device, location, preferences, and more. Search engines provide top-of-the-line results by moving past the basic protocol of keyword matching, which might require users to adjust their search to find what they're looking for.

Modern search engines utilize what's called natural language search. Natural language search enables users to use a more familiar form of communication with the search engine, granting more casual search queries to return with accurate and useful results.

Here, our team at Yext talks about what natural language search is and how it affects your online company.

What Is Natural Language Search?

Natural language search (NLS) is a complex virtual process that utilizes a tool called Natural Language Processing (NLP). An NLP is a form of artificial intelligence that collects and processes mountains of previous searches and related data to determine a user's intention and personal qualities, allowing it to supply extremely specific search results to any question.

With the advancement of processing and computing abilities, our search engines have been able to expand far beyond where they began. Search engines today are more accurate and complex than they were even ten years ago.

Natural language processing scans each element of a search string individually as well as in the context of the other terms.

For instance, say you were to type in "Where can I order dinner tonight?"

Natural Language Processing would determine that you, at your location, we're looking for dinner, an evening meal, tonight, the evening that you searched. Then, cross-referencing recent searches for specific restaurants with what restaurants are open in the time frame you're looking for, NLP would be able to supply you with extremely relevant results.

What Came Before Natural Language Search?

Before the year 2009, major search engines utilized meta keywords. The technique at the time would analyze each word in the search entry and find the most relevant results based on the amount of matched keywords.

They would also use other parameters, but the system was inherently flawed for several reasons. First, users would naturally want to type in actual questions, but the search engine would get bogged down looking for results that include the extra words.

To use our example above, if you searched "Where can I order dinner tonight?" A meta keyword-based search engine would take each term and search for articles with high-match rates.

This means that, while you really just wanted to look for "dinner," "tonight," the search engine would include articles with the words "where," "can," "I," and "tonight." So, you would end up with results that weren't relevant to what you intended.

This leads to one of the main reasons that major search engines abandoned keyword search methods. Developers realized that if they cram their keywords into the meta tags, their articles were more likely to reach the first page due to the high volume of matches.

This tactic of keyword blasting was causing completely irrelevant pages to show up during search results, so meta keywords were abandoned, and other meta tags were focused on.

But NLP allows search engines to move even further beyond reading meta tags to the point where they can use them in a real-world context—allowing the search engine to actually understand the user's question better and thus return stronger results.

Where is Natural Language Search Used Today?

Most major search engines today utilize NLP. One of the most commonly referenced phenomena for NLS advancement is probably sitting in your pocket or on your desk right now.

With the development of smartphone technology, society has become used to casually speaking into their phone whenever they have a question. However, the growing popularity of smart assistants like Siri and Alexa is a chicken-egg situation when it comes to NLS.

Because people wanted to speak with these assistants, the demand for NLS grew. And because NLS got better, the usage of virtual assistants continued to rise.

Today, people can casually ask their assistants a question they would like a normal person, and by normalizing this relationship with technology, search engines had to keep up with user expectations. In fact, virtual assistants have become so popular that half of all search entries are submitted vocally through a mobile device or pod system.

The Next Step in Natural Language Search

While NLS is incredibly impressive, there's still. Further, it can expand.

As it stands, most major search engines consider each search entry just that; individual and separate from the one before it, aside from analyzing search result information.

The concept of extended conversation search is the next movement, and it's already happening.

By extended conversation, we mean the search engine can understand if two questions are related and retain information from the first to answer the second.

For example, say you type, "How tall is Bono," into a search engine. The results would come up quickly to tell you that he is five foot six inches tall.

Then you typed in "Where does he live?" Because you only typed in "he" instead of "where does Bono live," most search engines will bring up strange articles that might have nothing to do with your initial query. They won't understand that you're still talking about Bono.

This is what we mean by extended conversational search. It does exist in certain major engines, and it very well may be the next step towards a truly conversational search experience.

How To Utilize Natural Language Search

A search engine optimization technician is an incredibly specialized and tactical position. And with major search engines changing and not releasing the details of how their algorithms analyze and choose articles, it can sometimes be a bit like shooting in the dark.

What we do know is that as NLS continues to advance and become more prominent, there are a few ways your company website can set itself up to be popular amongst search queries.

Primarily, focus on creating in-depth and detailed content. While NLS doesn't rely on meta keywords like search engines before, it still looks for phrases that relate to the search input.

Suppose, for instance, you have a photography business that specializes in weddings. You might want content on your website that included popular search phrases such as "who are the best wedding photographers in Los Angeles?" Instead of simple key phrases like "wedding" and "photographer."

Another strong idea is to feature potential search questions on a Frequently Asked Question (FAQ) page. This is a more natural way to include potential search queries. For instance:

  • Why should I hire a wedding photographer?
  • How much would it cost to hire a wedding photographer?
  • Can the wedding photographer travel outside of Los Angeles?

Including the conversational questions that users might ask straight to their engines as well as resourceful and accurate answers provides the content that NLS is looking for as its ranking results.

In Conclusion

The goal of every search engine is to supply its users with the most accurate and relatable results it can, and natural language search is the next step in that mission.

Far exceeding the days of Archie and the endless cramming of meta keywords into HTML or Javascript codes, natural language search allows users to ask questions comfortably and expect strong answers.

Technology and the internet are moving towards a stronger connection with human users. For example, search engines strive to analyze a search query as a string of words and understand the question being asked within the content of the user's personal experience.

Whether it's typed out or spoken into a device, natural language search is another piece that bridges the communication gap between humans and technology, and it's only getting better from here.

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