Search Intent Classification Using Natural Language Classifier
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Solution Overview
Problem
Current search query technologies face inefficiencies in accurately selecting search parameters based on user input, with a complex and resource-intensive process for analyzing search result parameters across various attributes.
Innovation Solution
A method utilizing a natural language classifier (NLC) circuit to generate an intent domain associated with subject-based intent classification, analyze search queries, compare them to search results, and rank relevant results, presenting a tailored and ranked list based on user intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current search query technology is used to select search parameters, then search results can be generated, but the process is inaccurate and lacks flexibility
Solution Approach 1:
The system dynamically changes search parameters based on detected user intent. Instead of using fixed search parameters, the system adjusts parameters such as query formulation, result filtering criteria, and ranking weights according to the identified intent domain (e.g., informational, navigational, transactional), thereby improving accuracy while maintaining flexibility
Solution Approach 2:
The search system transitions from a static parameter selection process to a dynamic one where parameters are adjusted in real-time based on user input analysis. The natural language classifier continuously monitors and reclassifies user queries, allowing the system to adapt search parameters dynamically to match evolving user intent
2Reliability
If search result parameters are analyzed with respect to various attributes, then comprehensive search results can be obtained, but the process becomes complicated and time consuming
Solution Approach 1:
The system segments the search result analysis by intent domain. Instead of applying comprehensive analysis to all search results uniformly, it divides results into categories based on detected user intent (informational, navigational, transactional, etc.) and applies targeted analysis specific to each segment, reducing overall processing time while maintaining comprehensiveness
Solution Approach 2:
The system performs partial analysis by focusing only on the attributes and parameters most relevant to the detected intent domain. Rather than analyzing all possible result attributes equally, it applies analysis selectively to the subset of attributes that matter most for the specific user intent, reducing time consumption while preserving result quality
3Measurement precision
If comprehensive search result analysis is performed, then accurate search results can be produced, but a large amount of resources are required
Solution Approach 1:
The system applies different levels of analysis quality to different search results based on their relevance to the detected user intent. High-priority results matching the intent domain undergo comprehensive analysis, while lower-priority results receive streamlined analysis, optimizing resource allocation while maintaining overall accuracy
Solution Approach 2:
The system performs analysis only to the extent necessary for the detected intent domain. For example, transactional queries may require detailed product attribute analysis while informational queries may suffice with summary-level analysis, reducing computational resource consumption while preserving necessary accuracy
Data Source
AI summary
A method and system for improving an Internet based search is provided. The method includes generating an intent domain associated with a subject based intent classification. An unstructured data analysis process is executed with respect to a content corpus being associated with the subject based intent classification and a search phase entered in a search field of a graphical user interface with respect to a domain specific search query for specified subject matter. In response the subject based intent classification is determined to be associated with the search query and the subject based intent classification is compared to search results data. A subset of search results of the search results data correlating to the subject based intent classification is determined and ranked resulting in a ranked list. The subject based intent classification and the ranked list are presented to a user.