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Information Literacy Guide

Learn all about different sources of information

Natural Language Searching

Natural Language Searching allows queries to be made to a database or search engine using everyday language, instead of requiring specialised terms or codes. Users can input questions or express concepts in a conversational manner, similar to how they would communicate verbally or in writing. The system then interprets these queries, finding relevant results based on the meaning of the words and the context of the question, rather than relying solely on exact keyword matches. This approach aims to simplify searching, making it more intuitive and user-friendly, particularly for individuals who may not be familiar with specific terminology or search techniques.

Natural Language Searching has benefits over Boolean Search:


  1. Intuitive: Users can use everyday language, making it easier and more accessible.
  2. User-Friendly: It's simpler, reducing the learning curve.
  3. Increased Relevance: It aims to understand user intent for more relevant results.
  4. Faster Searches: Users can quickly formulate queries.

However, it also has drawbacks:


  1. Ambiguity: Natural language queries may be unclear, leading to irrelevant results.
  2. Lack of Precision: Queries may lack specificity, resulting in broad results.
  3. Dependency on System: Effectiveness relies on the system's ability to understand queries.
  4. Limited Control: Less control compared to Boolean Search.
  5. Language Limitations: May struggle with multiple languages or slang terms.

In short, while Natural Language Searching is easier to use, it may produce less precise results compared to Boolean Search.

Google Scholar employs natural language processing (NLP) techniques to enhance its search capabilities. While Google Scholar's exact algorithms and methods are proprietary and not publicly disclosed, it's widely understood that NLP plays a significant role in understanding and interpreting user queries, identifying relevant academic content, and improving search result accuracy. NLP helps Google Scholar to comprehend the intent behind user queries, analyse academic texts, and provide more relevant search results to users.