What is Natural Language Search? How AI is Transforming Search

What is Natural Language Search?

Natural Language Search (NLS) is a type of search interface that uses Artificial Intelligence (AI) and allows users to query data in natural language rather than using structured queries like SQL, keywords or specific query syntax. NLS relies on Natural Language Processing (NLP) techniques to interpret and understand user queries, extracting meaning, context, and intent from it so that the system can provide accurate and relevant results. NLS harnesses the power of NLP to translate a user’s natural-language input into the structured commands or data filters needed to retrieve information from a database or search index. NLS is designed to simplify interactions with databases, search engines, or other information systems by making it more accessible and intuitive for non-technical users.

Example of Natural Language Search

For example, imagine you're planning a trip and want to find a hotel with specific amenities. Using a traditional keyword-based search, you might input:

This search could yield results that include any or all of these terms, but it may not accurately capture your specific requirements. With Natural Language Search, you can enter a more conversational query:

The NLS system processes this query by understanding the context and intent behind your words. It recognizes that you're looking for hotels offering specific amenities in your vicinity. By interpreting the natural language, the system can provide more accurate and relevant search results that match your criteria. This approach enhances the user experience by allowing searches to be conducted in a more natural and conversational manner which reduces the need for users to formulate precise keyword combinations and also gives better results.

Keyword Search vs Natural Language Search

NLS and Keyword Search are two different approaches used in information retrieval. Each search is different in how it interprets user queries and delivers results.

Keyword Search

Keyword search relies on matching specific words or phrases entered by the user to indexed content. The user input needs to be concise and only targeted keywords must be used to get best results. The results depend on exact keyword matches as it may return irrelevant results if the exact terms aren't present. Following is the example of keyword search:

• User Query: "best Italian restaurants NYC"
• Interpretation: Searches for documents containing the exact phrases "best," "Italian," "restaurants," and "NYC."

Natural Language Search (NLS)

NLS uses NLP to understand the intent and context behind a user's query to enable more conversational and intuitive searches. User input can be full sentences or questions that are similar to natural human communication. The results are based on the interpreted meaning and it may give better results even if exact keywords are not present. Following is the example of NLS:

• User Query: "Where can I find the best Italian restaurants in New York City?"
• Interpretation: Understands the user's intent to locate top-rated Italian dining options in NYC, considering synonyms and contextual relevance.

Keyword Search vs NLS 

Let us the differences between NLS and Keyword Search with more detailed explanations, examples, and insights into how they function.

Input Style and User Experience

In keyword search input requires specific terms or phrases to be searched. Users must guess or anticipate the exact keywords likely to be present in the indexed content. If the user does not know the exact terms, results may be irrelevant or incomplete. Consider the example below.

NLS allows users to specify full, conversational sentences or questions. It is designed to mimic how people naturally ask questions. Users don’t need to think about the exact wording, as the system interprets the intent of the query. For example:

Processing and Understanding

Keyword search uses basic string matching or pattern recognition to locate results. It lacks the ability to understand relationships between words or interpret intent behind query. It sometimes struggles with synonyms or variations of phrases. For example, it treats “NYC” and “New York City” as different entities. For example:

NLS uses NLP, which breaks down and analyzes queries to identify key entities (e.g., “Italian restaurants” and “NYC”) and also understand the query intent (e.g., finding dining options).

It also uses synonyms and alternate phrasing and recognizes relationships between words and responds to implied meanings. For example:

Relevance of Results

Keyword based search matches results based on the presence of keywords in the search index. As a result it may return irrelevant results if keywords are vague or used in unrelated contexts. It also struggles to prioritize results based on the query’s implied importance. For example:

NLS interprets the user’s intent and retrieves results that align with the overall meaning, not just word matches. It understands implied context, such as “best” indicating user interest in recommendations and also ranks results based on semantic relevance and quality. For example: 

Handling Complex Queries

Keyword search works well for short queries but struggles with complex queries that involve relationships between multiple concepts. For example:

NLS excels at complex, multi-faceted queries by understanding relationships between criteria (e.g., hotels, free Wi-Fi, pool, beach, California). It filters and ranks results to prioritize user preferences. For example: 

Keyword Search has been foundational in information retrieval. However, Natural Language Search offers a more intuitive and user-friendly experience by understanding the intent and context of user queries. This leads to more accurate and relevant search results, enhancing overall user satisfaction.

How Does Natural Language Search Work?

Following are the steps involved in NLS  which enables the search system to interpret and respond to user queries phrased in everyday language.

Query Analysis and Intent Recognition

This step involves understanding what the user wants to achieve with their search. It not only considers the words but also seeks to understand the underlying purpose or goal of the query. For example:

Entity Recognition

In this step, the specific piece of information (entities) is identified within the query, such as names of people, places, dates, or products. This helps in focusing on exactly what the user is referring to. For example:

Entity Recognition (Source)

Semantic Understanding and Context Interpretation

In this step the meaning behind the words in the query is understood by considering context, word relationships, and nuances. This ensures that the system understands the query as a whole, rather than just individual words. For example: 

Query Expansion

This step involves enhancing the original query by adding related terms or synonyms to improve search results. This helps in retrieving information that might be relevant but expressed differently. For example: 

Information Retrieval

In this step, the databases or indexes are searched to find content that matches the query of the user. This is where the system gathers potential answers or relevant information. For example: 

Example of Information Retrieval

Ranking and Relevance Scoring

This step involves evaluating and ordering the retrieved information based on how well it matches the query of the user as well as his intent. Higher relevance scores indicate more pertinent results, which are then presented first. For example: 

Presentation of Results

In this step the ranked information to the user is displayed in a clear and accessible manner with summaries, images, or direct answers to enhance user experience. For example: 

AI based search result presentation 

Continuous Learning and Feedback Integration

This step is responsible for providing feedback to improve future search accuracy and relevance by learning from user interactions. This involves updating algorithms based on what users find helpful or unhelpful. For example: 

By understanding and implementing these components, NLS systems can effectively interpret user queries and provide accurate, contextually relevant results and enhance the overall search experience.

Application of Natural Language Search

There are many applications of NLS systems in various domains. Following are some of the examples of how NLS systems redefine search in these domains.

E-commerce Platforms

NLS allows customers to search for products using natural language queries which provides desired results and improves user’s the shopping experience. For example, a user can type "comfortable running shoes under $100" and receive relevant product suggestions.

Product Search (Source)

Virtual Assistants and Chatbots

NLS enables virtual assistants to understand and respond to user queries conversationally.

For example asking Siri or Alexa, "What's the weather like today?" prompts a weather update.

Alexa - a virtual assistant

Healthcare Information Systems

NLS assists healthcare professionals in retrieving patient information or medical records using natural language queries. For example, a doctor can query, "Show me the latest lab results for John Doe." and the system comes up with the specific records.

Educational Platforms

Students can use NLS to find study materials or answers to academic questions. For example, typing "explain the theory of relativity" yields educational resources on the topic.

Customer Support Services

NLS enhances customer service by allowing users to describe issues in their own words which provides efficient problem resolution. For example, a user can state, "I'm having trouble logging into my account," and receive targeted assistance.

Content Management Systems

NLS helps users locate documents or media files within large databases using natural language queries. For example, a user may ask "find the latest marketing presentation"  to the search system and retrieve the relevant file.

Search Engines

NLS improves search engines by interpreting user intent behind complex queries thus providing better and relevant search results. For example, entering the query "best places to visit in Europe in spring", the NLS search engine provides best travel recommendations.

Bing Search Engine

The Role of AI in Transforming Search

AI has transformed search technology and the way users interact with information retrieval systems. With the help of advanced machine learning, natural language processing, and data analysis techniques, AI enhances the ability of search engines to understand, interpret, and provide accurate results. 

NLS to search multimodal files in Encord (Source)

Understanding Natural Language Queries

Traditional search relies on matching keywords in queries with indexed content which often leads to irrelevant results for vague or complex queries. AI-powered search uses NLP to understand the intent and context behind queries. It allows users to search in conversational language which makes the process more natural.

Personalization

Traditional search provided generic results with limited customization. AI based search analyzes user behavior, preferences, and past interactions to personalize search results.

Factors such as location, search history, and device type are used to enhance responses.

Semantic Search

Traditional search focused on exact keyword matching which sometimes missed the context. AI based search understands the meaning behind words and their relationships within a query. Synonyms, paraphrases, and context are considered to provide more relevant results.

Visual Search

Traditional search relied on text-based queries only. Use of computer Vision enables users to search using images also. AI analyzes visual content, recognizes objects, and provides information or matches.

Voice Search

Traditional search required users to type queries manually. NLP powers voice recognition systems which allows users to ask questions using voice commands. AI converts spoken language into text, processes it, and provides responses.

Conversational Search

Traditional search offered static, one-time results. Conversational AI enables ongoing, interactive dialogues, refining search results in real time. Users can ask follow-up questions without rephrasing or starting over.

Multimodal Search

Traditional search was limited to single-mode inputs (e.g., text only). AI supports multimodal search, combining text, images, and voice inputs for more dynamic queries.

AI-Generated Summaries and Answers

In the results from traditional search users are required to sift through links to find answers.

AI based search generates concise summaries or direct answers to user queries using Generative AI models and also provides links to resources.

AI is transforming search into an intelligent, personalized, and context-aware experience. By integrating NLP, AI based search systems provide accurate and meaningful search results. This shift is redefining how we access and interact with information across industries to enhance productivity and satisfaction.

How Encord helps build or fine-tune search models

Encord is a powerful data annotation platform that plays a vital role in building and fine-tuning search models especially for NLS systems. By providing tools for creating high-quality NLP datasets, Encord ensures search systems are accurate, efficient, and contextually aware.

Comprehensive Document and Text Annotation Tools

Encord offers tools that support text annotation tasks such as sentiment analysis, question answering, and translation to accurately label documents and text. By creating accurately labeled datasets, search models can better understand and process natural language queries which provide more accurate and relevant search results.

Integration of State-of-the-Art Models

Encord allows the integration of advanced models like GPT-4o and Gemini Pro 1.5 into data workflows to automate and accelerate the annotation process. Using these models enhances the quality and consistency of annotations and provides a solid foundation for training search algorithms capable of understanding complex queries.

Customize multimodal data workflows in Encord

Multimodal Data Management:

Encord enables the annotation of multimodal data types such as text, images, and documents, within a single platform. This capability is crucial for developing search models that need to process and retrieve information across different data formats, ensuring comprehensive search functionalities.

Annotating of document, image and video in Encord

Customizable Annotation Workflows:

Encord provides customizable workflows and quality control tools which helps in customizable annotation processes that meet specific project requirements. Customized annotation workflows ensure that the training data aligns closely with the intended use cases of the search model. This improves the performance of search models and their relevance in real-world applications.

Fine-Tuning Foundation Models:

Encord offers resources and tools to fine-tune foundation models, such as Meta AI's Segment Anything Model (SAM), to specific applications. Fine-tuning these models with domain-specific data enhances their ability to understand and process specialized queries which leads to more precise and effective search outcomes.

The NLP data annotation capabilities offered by Encord enables development and refining search models that are more accurate, context-aware, and responsive to user queries which as a result helps in enhancing the overall search experience provided by NLS search engines.

Key Takeaways: Natural Language Search 

• NLS allows users to interact with search systems in conversational language. NLSuses  NLP to understand user intent and context and offer more accurate and relevant results compared to traditional keyword-based searches.
• Keyword searches rely on exact matches and may return irrelevant results if terms don’t align perfectly. NLS, on the other hand, interprets user intent and considers synonyms, context, and relationships between words to provide meaningful results.
• NLS simplifies complex queries by understanding relationships between multiple criteria and delivering precise results.
• AI has revolutionized search systems by enabling features like semantic understanding, voice and visual search, personalization, and multimodal search. It ensures results are meaningful, context-aware, and tailored to individual user needs.
• NLS is widely used in e-commerce, virtual assistants, healthcare, education, customer support, and content management. It allows users to interact naturally and improve search accuracy and relevance.
• Encord facilitates the development of NLS systems by providing robust annotation tools, multimodal data management, and customizable workflows. It enables the creation of high-quality datasets and fine-tuning of foundation models to build contextually aware and highly responsive search systems.