What is Natural Language Search?

Natural language search is a supremely human way to search that involves speaking or typing a query into a search application. By “query” we mean a conversational prompt. It can be a question like “When is the ideal time to plant tulips?” but it can also be a command like, “tell me today’s weather” or “show me how to get to the nearest Starbucks.” 

Natural language queries reflect a more intuitive way for people to search versus traditional keyword search. Rather than type in an exact keyword or phrase, the searcher poses a question or states a command in their native language, as if they’re talking to another person versus a machine.  

Natural language search engines respond to conversational queries using an AI-powered approach called natural language processing (NLP) which enables them to understand the context and meaning of our speech and text. They’re able to produce relevant results because they understand the language in ways that traditional search engines can’t.

How Does Natural Language Search Work?

Natural language search uses NLP to analyze our conversational queries (words, phrases, sentences, etc.) which, from the perspective of the search engine, is unstructured data. It then uses complex algorithms to understand the meaning and intent behind a search. For example, removes filler words, recognizes when a word is mispelled, understands synonyms, identifies and interprets different languages, and can also glean meaning from word roots and parts of speech.

Let’s break down a natural language query.

If you speak or type, “How long do nectarine trees live in colder climates?” Here’s how NLP might break down this sentence so that it understands your intent and can produce the correct results:

• How long – identifies this as a question related to time/duration or lifespan
• nectarine trees – identifies as the subject of the query, likely using named entity recognition to understand that this refers to a specific type of fruit tree.
• live – Is interpreted semantically in the context of duration. This semantic relationship is how the search engine know that “live” refers to how long the tree typically “lives” versus where it lives or why it lives.
• in colder climates – a crucial piece of information that modifies the query, specifically the conditions under which the lifespan is being asked about.
• filler word – removes the filler word “do” which doesn’t add meaning to the query.

Breaking down the query into the various bits and pieces of language is very similar to the way humans understand language. It’s what enables a natural language search engine (and a human) to understand the intent of the person asking the question. 

In this case, it’s to find information about the lifespan of nectarine trees in cold weather conditions. From a user perspective, it’s easy. We ask the question in a way that’s natural, without worrying about the specific keywords, phrasing, or spelling.

Natural Language Search Examples

There are many examples of natural language search that you’re likely familiar with either from your own day-to-day search activity or in your business/work life. Some of the top use cases include:

1. Voice assistants and smart speakers 

Voice assistants like Siri, Alexa, Google Assistant, and Cortana use natural language search to understand our search intent and provide relevant responses. It’s this technology that makes it possible for Google to give you driving directions or Siri to tell you the day’s weather. 

2. Product search 

Retailers use natural language search to help customers find relevant products on websites and shopping apps – an approach also known as “searchandising”. They help make ecommerce experiences more satifying and relevant for shoppers. For example, an online beauty retailer might use a predictive autosuggest feature to match relevant skincare products to a searcher’s shopping profile or browsing history. NLP-enabled search can also find products that match a query even when there’s not an exact match in the retailer’s catalog or if the user misspells the product name.  

3. Conversational AI chatbots 

Chatbots and virtual assistants often incorporate NLP to enable natural language search. As with voice assistants like Siri, it’s what makes conversational search tools like Microsoft Copilot understand your queries and produce humanlike responses. The difference is that conversational chatbots process text queries versus voice queries. Since chatbots with NLP can understand and process complex questions, they tend to be used for customer service and support. They can engage in more human-like conversations with website customers, help them find appropriate products, and route issues to the right person or resource. 

4. Enterprise search

Companies are increasingly implementing natural language search as part of their enterprise search to make it easier for employees to find information across various internal systems and databases. This involves connecting various databases and tools to make the content searchable. It’s an indispensable tool for remote workers, new employees, and customer service workers who need quick and easy access to up-to-date files and assets across their organization’s entire information ecosystem.

5. Personalized search

 Personalized search engines tailor search results for each user by analyzing real-time behavioral and historical data and query context to understand search intent. It then provides contextually relevant results. For example, personalized ecommerce search leverages shopper data like queries, clicks, and user location to present relevant products to the customer. It can also prioritize product positioning in the search results, allowing retailers to highlight various products based on their merchandising strategy or availability. 

A Brief History of Natural Language Search

The first application of NLP technology for search was the START Natural Language Question Answering System, created in 1993 by the MIT Artificial Intelligence Lab. While not a web search engine, it allowed users to query an online encyclopedia using natural language. 

In 1996, Ask Jeeves, later rebranded as Ask.com, launched as the first web search engine to use natural language processing. Google entered the scene two years later and quickly dominated the search market. However, it wasn’t until 2019 that Google introduced BERT, a significant improvement in its ability to understand the context and nuance of search queries.

The next step in NLP was T5, or Text-to-Text Transfer Transformer, developed by Google Research in 2020. Unlike previous models that treated different language tasks separately, T5 takes a unified approach by framing everything as a text-to-text problem. T5 handles both input and output as plain text, a consistency that allows it to tackle a wide range of language tasks (translation, summaries, answering questions, etc.) more efficiently and effectively than ever before.

Since the introduction of T5, researchers are continuing to push the boundaries of NLP. Future developments may include models with deeper contextual awareness. For example, they may be able to remember and reference previous interaction. They could also have more human-like learning capabilities, including the capacity to learn and adapt from their own conversations. Your next best friend might very well be an NLP-powered search engine.

Keyword Search Vs. Natural Language Search 

Keyword search puts keywords and phrases at the center of search. It’s about specifics – a user types in an exact term or phrase into a search engine and the engine searches for a document or source that contains the keyword. 

For example, if you wanted to find new running shoes, you might type “running shoes” into the search bar. Users need to think carefully about which keywords to use and often resort to “computer speak” to get decent results. If the search engine can’t match the keyword to a piece of content, it may come up with blank results or irrelevant results.

In contrast, natural language search lets users pose questions or commands as if they were speaking to a human. Instead of “running shoes,” you could search “I need sneakers for running a marathon.” A natural language search engine interprets meaning and intent of this query, even if it contains slang or misspellings. It delivers results even if they’re not an exact match or it makes relevant suggestions based on the query (again, even if the source document doesn’t contain terms or phrases that exactly match the query). 

Semantic Search Vs. Natural Language Search 

With semantic search, the search engine goes beyond matching the keyword literally to the results. It interprets the contextual meaning of a query or search term.  So, for example, a search for “swift information” may produce results about Taylor Swift versus results about Chimney Swifts (the bird) based on searcher behavior and context like past searches and location. 

Natural language search makes semantic search even more relevant by accounting for meaning and relationships between terms then using NLP to understands fully-formed questions or phrases. The two work together, with modern search engines utilizing semantic analysis as part of the NLP process.

However, not all semantic search involves processing natural language queries. Natural language search engines must employ semantic analysis to derive meaning from how users verbally express queries.

What is a Natural Language Processing Search Engine?

A natural language processing search engine understands queries expressed in everyday human language, rather than just keywords. It uses NLP technology to interpret the meaning and intent behind conversational phrases or questions.

For example, if a user searches “men’s shoes for a wedding”, an NLP search engine understands the context of the search – that is, they want shoe options that are appropriate for a formal event versus sneakers or hiking boots. 

Benefits of Natural Language Search

Natural language search is all about providing relevant results akin to what searchers have become used to thanks to websites like Google, Amazon, and Netflix. It’s ultimately about allowing users to search in a way that’s intuitive and produces personalized results with benefits that include: 

• Improving the customer or shopping journey across digital channels. Natural language search makes it easier for users find to find the information they need quickly. 
• Answering complex queries that traditional search can’t handle, like “Show me garden centers near me”  or “which birdseed attracts cardinals and finches?”
• Understanding search intent based on context since NLS can derive the underlying meaning of phrases without specific questions attached. For example, “gerbera daisies on sale” or “fastest route to midtown” versus, “Are gerbera daisies on sale locally?” or “what’s the fastest driving route to midtown from my home?”
• Grasping context and synonyms – NLS reduces zero-result rates or wildly inaccurate results, both of which frustrate users.

Ultimately, these combined benefits lead to better overall website performance like higher average order values, better inventory management, and improved customer satisfaction.