Search Ranking via Natural Language Query Pattern Analysis
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Solution Overview
Problem
Search systems face challenges in accurately identifying and providing factual answers to user queries, as existing methods lack effectiveness in distinguishing between fact-seeking queries and other types of queries, leading to suboptimal ranking and presentation of search results.
Innovation Solution
The system determines the likelihood of a query being fact-seeking by analyzing query patterns, specifically using a natural language ratio from query logs to identify and rank search results, providing a factual answer when the likelihood meets a threshold, and presenting it prominently in search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the system provides factual answers for all queries, then user information needs are met, but irrelevant information is presented to non-fact-seeking queries
Solution Approach 1:
The patent segments queries into different types by analyzing query patterns and characteristics. It identifies fact-seeking queries versus non-fact-seeking queries through pattern matching and statistical analysis, then applies different response strategies to each segment, providing factual answers only where appropriate and search results for others.
Solution Approach 2:
The system dynamically determines the appropriate response type based on real-time query analysis. It evaluates query patterns, user behavior, and contextual factors to adaptively decide whether to provide a factual answer or traditional search results, making the information presentation flexible and context-aware.
2Measurement precision
If the system uses complex analysis to identify fact-seeking queries, then accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary analysis of query patterns by pre-processing and categorizing queries based on their characteristics. It establishes query pattern databases and classification rules in advance, enabling faster identification of fact-seeking queries during actual search operations without requiring complex real-time analysis.
Solution Approach 2:
The system introduces an intermediary classification layer that sits between query reception and answer generation. This intermediary component analyzes query patterns and determines the appropriate response type, separating the complex analysis task from both query input and answer output, thereby optimizing overall processing efficiency.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining whether to provide a factual answer to a user's query. In one aspect, a method includes obtaining a set of query patterns associated with a particular fact. A subset of natural language query patterns can be identified from the set of query patterns. A likelihood that a query corresponding to at least one of the query patterns from the set of query patterns is a fact-seeking query can be determined based at least in part on comparing a first query count associated with the subset of natural language query patterns with a second query count associated with the set of query patterns. The likelihood can be provided as a signal for ranking search results.