Search Result Analysis Using NLP for Query Refinement
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Solution Overview
Problem
Search engines often fail to provide relevant results, leading users to perform additional searches or revise queries, as current systems lack the ability to effectively utilize initial search results to generate customized and efficient search outcomes.
Innovation Solution
A computer program product and system that employs natural language processing techniques, including machine learning models, to analyze initial search results, generate derived queries, and score secondary results based on semantic comparisons, thereby improving search efficiency and relevance by filtering redundant features and categorizing results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional search engines are used to search for information, then users can access available information, but users must perform multiple searches or revise queries when initial results are not relevant
Solution Approach 1:
The system performs preliminary analysis of initial search results to identify categories and generate derived queries before the user needs to conduct additional searches. By pre-processing the results and anticipating follow-up search needs, the system eliminates the time users would otherwise spend performing multiple sequential searches.
Solution Approach 2:
The system uses the initial search results as feedback to automatically generate refined derived queries and categorized results. This feedback loop allows the system to learn from the initial search outcomes and improve subsequent search operations, providing more relevant results without requiring users to manually revise their queries.
2Quantity of substance
If search engines provide comprehensive results, then more information is available, but results include redundant features and lack customized organization
Solution Approach 1:
The system segments the comprehensive search results into distinct categories based on analyzed features and characteristics. By dividing the large set of results into organized categories, the system maintains the quantity of available information while making it easier for users to navigate and find relevant content without being overwhelmed by redundant or unorganized data.
3Reliability
If users perform multiple searches to find relevant information, then more comprehensive coverage is achieved, but search efficiency decreases
Solution Approach 1:
The system merges the functionality of multiple sequential searches into a single automated process. By combining the initial search results with automatically generated derived queries and category-based organization, the system achieves comprehensive information coverage that would otherwise require multiple user-initiated searches, thereby significantly improving search efficiency.
Data Source
AI summary
A set of first results from a first query are received. Using a natural language processing technique, a set of second results using a second query having at least a portion of the first query and at least a portion of set of first results are generated, wherein the natural language processing technique comprises a machine learning model configured to analyze the set of first results. The set of second results are scored based upon a semantic comparison of the set of first results, the second query, and the set of second results. Using the scored set of the second results, a set of processed results are generated.