Do you ever wish you could just search for information online as if you were sending a message to your best friend?
Well, it’s actually possible if a website has a natural language search engine.
Natural language search engines are made to understand conversational language so that users can express themselves naturally and get the most relevant information from their search queries.
Long gone are the days of searching with a few keywords that end up leading you to a variety of irrelevant search results.
In this article, we’ll explore some of the history of natural language searching, take a look at some examples, and discuss how you can improve your website with a natural language search engine.
Let’s get started!
Table of Contents
- What is Natural Language Search?
- A Brief History of Natural Language Search
- How does natural language searching work?
- Natural Language Examples
- Natural Language Search Best Practices and Tips
- 1. Understand your customers
- 2. Use customer-friendly language
- 3. Provide clear instructions
- 4. Integrate Artificial Intelligence
- 5. Offer helpful suggestions
- 6. Understand synonyms and related terms
- 7. Be flexible with how queries are asked
- 8. Make search filters easy to use
- 9. Learn from customer interactions
- 10. Continuously improve and refine
- How to Integrate a Natural Language Search Engine into Your Website
What is Natural Language Search?
Natural language search is a way for people to find information using everyday language instead of specific keywords or phrases.
It aims to make searching easier and more intuitive by allowing you to type or speak a question or a sentence as if you were asking a person.
Instead of having to think about what keywords to use, you can simply ask a question or describe what you’re looking for in a natural way.
A Brief History of Natural Language Search
Back in the early days of search engines, finding what you needed meant carefully choosing specific keywords.
It was like solving a puzzle, trying to match the right words for relevant results.
But as technology advanced, people started to want a more intuitive and user-friendly search experience.
So, in 1993, the MIT Artificial Intelligence Lab created the START Natural Language Question Answering Machine.
It wasn’t a web search engine, but it used Natural Language Processing (NLP), which was the game-changer that started to pave the way for natural language search.
Jump to 1996, and the first natural language search engine on the internet, Ask Jeeves, was finally released.
Using NLP algorithms, this web search engine could understand the meaning and context behind natural language search queries, going beyond simple keyword matching.
A few years later, Google joined the party and revolutionized search as we know it.
Then came the rise of voice assistants and smart speakers, and suddenly, we started talking to our devices like we were chatting with a friend.
We asked questions, gave commands, and expected them to understand us just like another person would.
Fast forward to today, and search engines and virtual assistants like Google, Siri, and Alexa have become masters of natural language processing.
They’ve honed their skills to comprehend complex queries, interpret our intentions, and deliver search results that hit the mark.
Natural language search bridges the gap between humans and technology, allowing us to communicate in a way that feels natural and familiar.
As natural language search continues to evolve, it empowers us to interact with technology on a more personal level, making the quest for knowledge easier and more enjoyable.
How does natural language searching work?
Instead of focusing only on specific keywords, natural language search engines try to understand the meaning behind your conversational search queries.
To do this, it uses natural language processing to look at the context of your question, consider synonyms, and understand how words relate to each other.
By doing so, the natural language search engine can accurately interpret what you’re trying to find.
For example, imagine you’re shopping online for running shoes and you come across a website with a natural language search feature.
You type in something like, “Hey, can you show me some affordable running shoes for women that have good arch support?”
It quickly presents you with a list of running shoes designed specifically for women.
And not only that, it narrows down the options to ones that are affordable and provides excellent arch support.
But here’s where it gets even better! If you’ve shopped on the website before or searched for similar items, the system takes that into account.
It suggests running shoes that match your style and preferences, making the whole experience more personal.
With the help of natural language understanding, the online store creates a friendly and conversational shopping journey, making it super easy for you to find the perfect running shoes that fit your needs and budget.
Natural Language Examples
Natural language searches are getting more popular because search engines are getting better at understanding what we actually mean and giving us the right results.
So, instead of just typing a few words, people are asking questions or using everyday language to find what they want.
Let’s take a look at some traditional search queries compared to natural language queries.
- Traditional Search Query: “Smartphones under $500”
Natural Language Query: “What are the best smartphones under $500?”
- Traditional Search Query: “Buy running shoes online”
Natural Language Query: “Where can I buy running shoes online?”
- Traditional Search Query: “Clothing discounts and deals for women”
Natural Language Query: “Where can I find discounts and deals on clothing for women?”
- Traditional Search Query: “Buy organic skin care products online”
Natural Language Query: “Where can I buy organic skincare products online?”
- Traditional Search Query: “Customer ratings of online grocery delivery services”
Natural Language Query: “What are the customer ratings of online grocery delivery services?”
Natural Language Search Best Practices and Tips
1. Understand your customers
Take the time to understand your customers and how they express their needs in simple everyday language. This will help you make your search engine better at understanding and responding to their queries.
2. Use customer-friendly language
Make sure your website uses language that your customers can easily understand. Avoid using complicated words or technical terms that might confuse them.
3. Provide clear instructions
Help your customers know how to use the natural language search on your website. Give them clear instructions or examples to guide them in asking their questions or describing what they’re looking for.
4. Integrate Artificial Intelligence
Make your search engine smarter by considering things like user preferences, location, and past interactions. This way, you can provide more personalized and relevant search results.
5. Offer helpful suggestions
Give your customers suggestions as they type to assist them in forming their queries. This autocomplete feature can guide them toward popular or commonly searched topics and help them find what they want faster.
Teach your search engine to recognize different words or phrases that mean the same thing. This will ensure that your customers can find what they’re looking for even if they use different words to describe it.
7. Be flexible with how queries are asked
Make sure your search engine can handle different types of queries, such as questions, statements, or descriptions for both text and voice searches. This flexibility makes it easier for customers to search in a way that feels natural to them.
8. Make search filters easy to use
Design your search filters in a way that is easy for customers to understand and use. Allow them to refine their search results based on things like price, category, or location.
9. Learn from customer interactions
Keep an eye on how customers use your search engine. Look for patterns and common questions or issues they face. This information will help you improve and make your natural language search even better.
10. Continuously improve and refine
Natural language search is an ongoing process. Regularly analyze user feedback, search analytics, and customer interactions to identify areas for improvement. Make updates and refinements to your search engine to enhance its accuracy and relevance over time.
How to Integrate a Natural Language Search Engine into Your Website
Now, it might seem like a big process to integrate a natural language search engine into your website.
But, what if I told you that there’s a search engine that uses advanced NLP technology to always find exactly what you’re looking for?
Doofinder, an advanced smart search engine, can be installed on your website in less than 5 minutes and doesn’t require any coding.
The best part is that you can try it out on your website for 30 days for free.
But, if you still aren’t convinced, you can always book a demo with one of our search specialists to learn more about it.
So what are you waiting for? It’s time to level up your website’s search engine!