Last Updated on April 15, 2024 1:21 AM IST
Today, searching on the internet has completely revolutionized, transitioning from keyword search to natural language interactions.
And this transformation has created a natural language search (NLS) vs. keyword search comparison.
This blog delves into the fundamental differences, examining input techniques, contextual understanding, result relevance, and overall search efficiency.
So, whether you are an experienced user or a newbie to online exploration, understanding these search methodologies allows you to use search engines effectively and get personalized results with ease. Let us begin the search.
Natural Language Search vs Keyword Search: What’s the Difference?
Keyword search is when you enter some words in the search bar and then the search engine finds web pages with those words.
It will search and match your words to its databases and get results for you. Conversely, today, search engines use AI to provide better results than regular keyword searches.
One of the popular example of using keyword search was ask.com which was a question answering page. Here, you need to enter a specific keyword to get the answer.
Simultaneously, NLP also helps you in your search. Instead of typing specific words in the search box you can ask questions or give commands in simple human language.
Today, even Google has significantly improved its search algorithms to understand natural language queries better. For example, when you ask, “What is today’s weather in Delhi?” Google will provide you with accurate weather information without using any specific keywords.
Input Technique
In a keyword search, you need to enter particular words or phrases to find what you’re looking for. While in natural language search (NLS), the system will use AI techniques such as natural language processing (NLP), machine learning algorithms, language inputs, voice commands, questions, and contextual details.
Hence, this allows it to provide more accurate and contextually relevant search results based on user intent.
Understanding the Context in Search
In keyword search, the system looks for exact matches between what you type and the keywords it has indexed.
But, on the other hand, the NLS system will understand the meaning and intent behind the search rather than just matching keywords.
This makes natural language search better, which will provide you with more relevant search results because it understands what you want. Hence, it provides a better search experience.
Natural Language Processing
Keyword searches do not use the natural language process; instead, they focus on specific words without understanding the complete sentence.
But in natural language search, the system uses NLP techniques to understand. This means they can understand and interpret questions written or spoken in natural language, making the search process more easy and user-friendly.
Result Relevance
Keyword search might simply showcase results depending on the entered keywords. It will not even consider whether they are exactly what you require or not.
On the other hand, an AI search system will use NLP techniques to understand your needs. Hence, this will provide you with more accurate and personalized results.
Better Contextual Understanding
The keyword search system only focuses on the words you enter and provides output. And sometimes it can lead to wrong or unrelated results. But NLS tries to understand the context of your search and provides you with better and more relevant search results.
Simplifying search with NLP
The keyword search will simply match the words, and it does not use any natural language processing techniques.
However, NLS is like having a daily conversation with your friend. It will understand what you are looking for. Simultaneously, it will use various techniques to figure out what you mean and thus provide you with better search results.
Personalized Results
Keyword searches give the same results to everyone. It does not consider what you are looking for. But natural language search will use AI techniques that will examine your previous searches and understand the way you search. At the end, it will showcase the results that are right for you.
It is like having a personalized search experience every time and for anything you search.
Online shopping ecommerce platforms such as Amazon and eBay have integrated natural language search capabilities.
Users can simply describe what they are looking for in simple terms, such as a blue dress with floral patterns. The platform will then generate the relevant search results and display them. Thus, it enhances the overall customer experience.
Responding to Complex Queries
Keyword search provides the best results for simple questions, but sometimes it can get confused with complex queries.
On the other hand, NLS is like having a smart assistant that can understand long and complex queries easily. It is skilled at handling complex queries and thus provides precise search results.
Conversational Search
Keyword search understands only short commands and does not support any conversational interactions. You cannot have a conversation or ask follow-up questions.
But in NLS it is like chatting with a friend who understands everything you say. You can seamlessly ask questions and get detailed answers. This makes your search experience more interactive and helpful.
Digital assistants such as Alexa, Siri and Google Assistant demonstrate conversational ability through the use of advanced technology known as natural language processing.
This allows them to understand complex questions, figure out context clues, and provide detailed responses. Users can easily communicate with these virtual assistants, asking additional questions or clarifying their questions to get exactly what they need.
This makes searching for information more like conversing with a helpful friend, which is more convenient and beneficial. Thus, using virtual assistants in natural language search makes searching more enjoyable.
Multimodal Input Support
Keyword search primarily relies on text input, but AI search systems can gain inputs including voice search, images, and even gestures. This means you can interact using various models, which makes the overall search experience unique and accessible across various devices.
Shufti labs verifies its users by combining text input along with image capture. It uses natural language for a streamlined search experience.
Semantic Search
Keyword search matches the search queries based on specific keywords or phrases. This leads to limited or irrelevant results if the keywords are not precisely aligned.
While NLS uses semantic search techniques that go beyond traditional keyword matching, It will analyze the meaning and context of the query. It will consider synonyms, related concepts, and user input.
Hence, this advanced technique enhances the search experience by delivering relevant and comprehensive results, even if the exact keywords are not present in the content.
We all know Spotify has a massive library of music, and it might be challenging for the user to search for their favourite music with traditional searching techniques.
To resolve this, Spotify R&D and engineering team uses natural language in its search. Users can now simply ask for music recommendations or specific songs as if they were talking to a friend.
This conversational approach allows for a more personalized and interactive music discovery experience. Furthermore, with the support for multimodal inputs, users can search using voice commands, images of album covers, or even humming a tune.
This semantic search capability ensures that users receive accurate results that align with their preferences, even if they don’t remember the exact song title or artist.
Overall, Spotify’s integration with NLS has revolutionized how users interact with their music platform.Thus with natural language search Spotify can understand the context behind the user’s search.
For example, if a user enters “relaxing music for studying,” then NLS will analyze the terms “relaxing” and “studying.” It will then recommend a playlist that is calm and focus-oriented.
Future Capability
Keyword search has limitations as it struggles to understand the context of your search. And, as the technology gets better, the keyword search will not be able to work better.
On the other hand, as AI search improves itself and gets better over time, and over the time NLS will be capable of providing you with the exact results you need.
According to Gary Illyes, Google Search Quality Analyst, “The future of search is natural language search. Search engines will need to understand the intent behind a user’s query, not just the keywords.”
Therefore, in the future, natural language search will be more popular than keyword search because of its intuitive and satisfying search experience.
The Top 5 Use Cases of Natural Language Search
Natural language search helps businesses enhance the overall customer experience. So, in the following section of the article, let us look at the top 5 real-life companies using natural language search.
Customer Service Chatbots
Many companies, such as Interpret, use NLS to revolutionize their overall customer service interactions.
They use NLS-powered chatbots, which function similarly to smart computer assistants. These chatbots understand how people speak naturally, even if their sentences aren’t perfect. This allows them to quickly determine what their customers want and respond with helpful information. It makes customers happier.
Language Learning Platforms
Memrise, a language-learning app, uses NLS to increase engagement. Learners can speak phrases to receive pronunciation feedback and upload images to generate vocabulary lessons based on content. It uses a smart search function that interprets natural language questions to deliver relevant learning materials, resulting in a more interactive and personalized experience that caters to various learning styles.
Ecommerce
E-commerce clothing retailers can use NLS to improve product search. Assume a customer looks for a “flowy dress perfect for summer evenings.” NLS analyzes this natural language query, looking beyond keywords like “flowy” to determine the intent (summer wear). This allows the search engine to recommend dresses with loose styles and breathable fabrics, giving the customer a more relevant selection, which increases the likelihood of a successful purchase.
Marketing
Natural language search (NLS) is transforming marketing by moving beyond keywords. Traditionally, marketers used static phrases, but today NLS examines user search trends. It identifies natural language phrases that people use to find products or services, revealing new keyword opportunities.
This enables marketers to tailor content and ad campaigns based on user intent, ensuring that they reach the intended audience. Furthermore, by incorporating these natural language terms, NLS assists in the creation of content that users actively seek.
Lastly, marketers can use NLS to personalise messages across channels, resulting in a more relevant and engaging experience for prospects. This all-encompassing approach using NLS boosts campaign reach, engagement, and conversion rates.
SEO Ranking
Greenify, a battery-saving app, was struggling with SEO for generic keywords. They used NLS to analyze user search queries and discovered natural language phrases such as “overnight battery drain.” Greenify then optimized content and incorporated NLS into their website to directly address these concerns. This resulted in higher rankings for relevant long-tail keywords, an improved user experience, and an increase in app downloads.
Summing up
Keyword searching has been with us for a long time, and it has been a standard method of finding information online.
But today, with Artificial intelligence, natural language search systems have taken the search to a new level. They understand the user context and use natural language.
This provides you with personalized and relevant results. Along with the development of technology, AI search is getting better over time, which provides a better outcome.
Overall, natural language search is becoming a game changer while searching for information online. It makes the overall search results more accurate and tailored to each user. Therefore, it will enhance our overall searching experience in the future.
In conclusion we are sure you might be clear between Natural Language Search vs Keyword Search keywords detailly and for such amazing content stay connected with Appskite.
FAQs
Does prompt engineering use natural language search or keyword search?
Prompt engineering uses NLS prominently rather than keyword search techniques. It is because natural language search helps in understanding the user intent and context better, which leads to more relevant and accurate responses. If you want to know more about prompt engineering, you can click here and download our prompt engineering cheat sheet.
Are there any disadvantages or limitations to using natural language search over keyword search?
One potential drawback or limitation of NLS is that sometimes it is challenging to understand the user’s questions. Even though technology like natural language processing has improved, sometimes the system might misunderstand a question and might not provide you with completely right answers, especially for technical topics.
Can keyword and natural language searches be combined to improve search accuracy and relevance?
Yes. The hybrid search technique combines keyword search techniques with natural search with the help of natural language processing. As a result, you will get both precise results and a better understanding of what you’re looking for, making your search more effective.
Learn about the benefits of natural language processing (NLP) and its use cases.
