Book a Demo

Please share your email and we will get back to you shortly.

30 day free-trial

Try Argoid for your business now!

Get product recommendation ribbons like 'Trending', 'Similar Products' and more, to improve conversion and sales Try Argoid, risk-free

Natural Language Search: How Does It Work?

Co-founder, CEO
Tags :
6 Mins

On the surface, search engines are straightforward yet intuitive. Intuitive search UIs allow you to enter your query and get the best possible results. However, a lot goes on underneath this exterior, especially when one factors in how we have grown to interact with our devices. Search is no longer restricted to typing.

On the contrary, you find yourself asking your phone’s voice assistant about the weather or telling the smart speaker to add items to your online shopping cart or speaking into your smart TV’s remote to play a song or a movie, or even making calls on command through your wireless earbuds - the list is endless! That’s not all.

According to Google, 27 percent of the online population across the globe is using voice search on mobile. A finding that unlocks a world of possibilities. And here’s where Natural Language Search makes its presence felt..

natural language search is different from regular search

What is Natural Language Search, and how are businesses leveraging it to their advantage? Let's take a look.

What is Natural Language Search?

Natural Language Search leverages the spoken form of communication to simplify how users operate the search engine. It achieves this goal by focusing on everyday, conversational language rather than on keywords. As a result, a natural language query would be much like any other question that you would ask a friend, a colleague, or any human, for that matter.

Meanwhile, the input received at the backend is processed by programmed logic to identify keywords and discover relevant matches for such a natural language query.

A Brief History of Natural Language Search

While personal digital assistants like Siri and Alexa have made natural language search mainstream, it was not an uncommon phenomenon even in the early days of the internet. In fact, it has been a work in progress for nearly three decades! 

NLP search made its first appearance as START Natural Language Question Answering System, developed by the MIT Artificial Intelligence Lab in 1993. While this innovation was not a search engine per se, it did a commendable job of allowing users to access online encyclopedia entries using natural language queries. From history and culture to geography to science to pop culture - this web-based question-answering system could handle them all.

Homepage of START Natural Language Question Answering System

Soon enough, Ask Jeeves attempted to be the Siri and Alexa of yesteryear. Debuting in 1996, the question-and-answer website built on the prowess of START  made headway on various topics to count as a full-fledged search engine.

Though AskJeeves was  ahead of its time, it was soon outdone by Google, which came about two years later.

Ask Jeeves Homepage

Now, coming to Google. Even though Google emerged as the ultimate victor amongst all search engines, it never really stopped innovating. And while it is notorious for changing algorithms, the announcement of Google BERT (Bidirectional Encoder Representations from Transformers) in 2019 was the final momentum required to popularize natural language search.

Google BERT

The BERT language model helped pre-train search engines in NLP. As a result, Google searches could now understand the context of any natural language query and use it to deliver sophisticated and accurate search results. And Google is not the only one heading in this direction; even Microsoft’s Bing offers smart search capabilities that can retain context to find matches for follow-up questions.

The Mechanism Behind Natural Language Search

An overview of how natural language search works

Natural language search rests on the principles of Natural Language Processing (NLP). NLP search employs a couple of techniques to comprehend the topic of the search rather than fixating on keywords. Here are some of the concepts that come into play:

  • Parsing: It involves splitting complex sentences into basic components to understand their meaning and identify the important words in the query. For example, “12 XYZ Street, Apt 8, ABC City, Texas 56789, USA” is parsed into: 

Building number: 12
Street Name: XYZ
Building section: Apartment 8
City: ABC
State: Texas
Zip: 56789
Country: USA

  • Stemming: It is responsible for deriving the root word or the stem. The algorithm removes the affixes and extracts the base or root word. For example, the base word for “searching” would be “search.” It helps cut down redundancies.
  • Lemmatization: It is a process where words with the same root are grouped together and mapped as a group rather than isolated terms. For example, the lemma or stem of the words swims, swam, and swimming is swim. As such, the search engine is capable of understanding the lemma, or the dictionary form, of a phrase and its variations rather than concatenating the individual meanings to give varying results. 
  • Named Entity Recognition (NER): Also known as entity identification or entity extraction, NER is the process of matching named entities with pre-configured categories. For example, the location entity in the sentence, Russia is the largest country in the world, would be Russia. It helps the machine extract key information and understand its meaning.

 When used on large volumes of data to run and train machine learning models, the meaning of complex, grammatically sound natural language queries as well as colloquial and conversational bits, can be inferred.

Natural Language vs. Keyword-Based Search

With the broad definition of natural language search out of the way, let’s take a look at how it differs from keyword-based searches.

How Natural Language Search is different from Keyword-Based Search

Anyone who has a modicum of interest in Search Engine Optimization (SEO) knows that keywords are the star of the show. These keywords break down the premise of an entire query into key focal points that help search engines identify the appropriate results. Most of us have been using it consciously or unknowingly. And, to be fair, it does make search queries short, succinct, and efficient.

So, why would one want to look up “what is the weather like today?” when they can simply search “weather today”?

The answer is simple - because keyword-based searches are not always reliable. They are effective primarily when the queries are structured and more or less basic, which, however, complicates the search for users. 

When it comes to layered or nuanced questions, keyword searches simply get watered down. For example, a keyword-based search of “bad egg” would yield a plethora of (irrelevant) results, while “how long does it take for an egg to go bad?” would be a highly specific natural language query that brings accuracy in results.

Natural language search not only unburdens the user from running structured, uncomplicated searches but also recognizes the underlying intent and insinuation that tints conversational language. So, users have to put very little thought into structuring a natural language query. In the simplest sense, natural language search is way more user-friendly than its keyword counterpart.

Prepping SEO for Natural Language Search

Now that it’s clear that natural language search is a force to reckon with and acts as an upgrade to keyword-based search, what does it mean from an SEO angle? Here are a few things of note:

Voice Search Explosion

We’ve already talked about how voice search is gaining traction amongst users. It is only a matter of time until it supersedes every other form of search and touches the top-tier of user preferences. And a shift in this direction makes sense; after all, voice searches are simple, accessible, and allow multitasking.

Also, the rising penetration of voice-activated devices will further propel voice search towards widespread acceptance. Given that all these searches will involve NLP searching, natural language search will soon be the norm across the length and breadth of the internet.

Long-Tail Keywords

 Illustration of short-tail to long-tail keyword

With keyword-based SEO, the focus primarily lies on a handful of keywords and maintaining a healthy amount of keyword density. However, as conversational language gains precedence due to natural language search, SEO would feature more long-tail keywords. So, rather than ranking for “dog food,” you might have to get more specific with the USP and try for “human-grade, grain-free premium dog food” to reach your target audience.

Such a move will attract an impressive ROI as you not only cater to voice-based searchers but also benefit from lower competition and higher conversion potential. As such, natural language search could serve as a level-playing field for businesses regardless of their size!

Factor in Related Keywords

Branching off from the previous point, SEO for natural language search should also account for related keywords. Related keywords are synonyms, variants, inflections, or semantically related terms linked to the primary or main keywords that add to the context of the search.

These can also be interwoven with questioning terms, that is, the 5W1H (Who, What, Why, When, Where, and How) that can even land you a sweet spot under the “People also ask” tab. Just make it a point to offer short, direct answers so that your website stands out.

Aim to Get Featured in Snippets

How Natural Language Search helps feature in snippets

Speaking of standing out, natural language search offers a unique opportunity for businesses to get featured in the snippets. In an introductory blog post about how Google BERT will reinvent search as we know it, the company shared several illustrations of how the search engine will pick up on the subtle nuances of human language that machines otherwise miss out on to reward user-friendly websites.

As a result, websites optimizing for natural language search are more likely to show up on Google Snippets. Furthermore, Google Voice search devices even read out the featured snippet content and put you in the spotlight.

Understand User Intent

Whether you try to rank with long-tail keywords or related keywords or optimize your website for BERT, at the heart of it all, you are trying to comprehend user intent. And with such a baseline expectation, the search engines must be given a boost using semantic context. Solutions like Argoid intuitively gauge whether the user aims to browse, learn, make a purchase, etc. This intent will allow you to categorize them into appropriate persona and push relevant results.

For instance, someone who is only searching to learn needs to be engaged and nurtured until they are ready for a sales pitch. Curating such unique buyer journeys will enhance the customer experience and drive conversions at accelerated rates.

Generate Informative Content

Tweet by Danny Sullivan explaining how content quality is NLP SEO

The best way to gain visibility in natural language search is by producing high-quality, informative, and natural-sounding content. Top-notch content lays the foundation for organically placing common keywords and phrases that might come up in natural language. At the same time, it follows a two-pronged strategy of matching the intent behind the search as well as satisfying the curiosity behind it.

Do be mindful of creating content that is geared towards answering user questions. From adding questions in your blog’s H2 to ending the post with a list of FAQs - there are several ways to funnel in traffic with the power of content.

Transcribe Non-Text Content

Non-text content, such as podcasts and videos, are goldmines for natural language search. These content assets would be naturally embedded with natural language keywords that can be hard to replicate in some cases. At the same time, rich media content continues to stay heavily in demand as customers find it easier to consume. Against this background, NLP search serves as the bridge connecting the two.

AI-powered search engines can attempt to translate audio clips or video snippets to understand them better and use this knowledge as a basis for ranking. Plus, you may reinforce it through transcriptions, meta tags, meta descriptions, etc., to climb the natural language search results.

Upgrade Your Tech Stack

Why use technology to add structured data

With natural language search relying heavily on semantics, you would have to revamp your tech stack. As seen already, AI-powered systems, such as that offered by Argoid, can supercharge your digital ecosystem and make it NLP search-ready.

Similarly, you would require an intuitive keyword detector that churns out natural language keywords. Apart from such conversational intelligence elements, you might have to review your Schema Markup and tag your website and its pages for heightened relevance and accuracy. The point is to optimize your website so that it offers the best answer to user questions.

Get Talking With Your Customers Using Natural Language Search

Natural language search is set to be the future of search and SEO. It is eliminating all kinds of barriers in voice searches and thriving on the pillars of user-friendliness and accessibility. Such a base props it up on the path to sustainable growth that will transform the way we search. Plus, given the rate at which the related technology is developing, NLP search will only get smarter, sharper, and more accurate.  

Try Argoid for your business

Zero setup fee . Comprehensive product . Packages that suit your business.