Multi-Hypothesis Search Query Intent Analysis
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Solution Overview
Problem
Traditional search systems face challenges in detecting and resolving search query ambiguity due to limited information at early stages, leading to suboptimal search results, especially in enterprise environments where personalized and contextualized content can exacerbate ambiguity.
Innovation Solution
The system generates multiple semantic hypotheses for search query intent understanding by evaluating search queries for ambiguity in domain, intent, and slots, using a combination of rule-based and machine learning models to provide ranked hypotheses for improved intent classification and query disambiguation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional search systems analyze only a single semantic hypothesis for a search query, then the system complexity is reduced, but the search result quality deteriorates due to inability to resolve query ambiguity
Solution Approach 1:
The patent segments the semantic analysis process by dividing the search query into multiple independent semantic hypotheses, each representing a potential interpretation. This allows the system to handle ambiguity by analyzing multiple possibilities separately rather than attempting to resolve all ambiguities simultaneously, thus improving search result quality while managing system complexity through structured decomposition.
Solution Approach 2:
The patent introduces a new dimension to search query analysis by moving from single-hypothesis analysis to multi-hypothesis analysis. This dimensional change enables the system to represent and process query ambiguity explicitly, allowing users to see and select from multiple possible interpretations, thereby improving search result quality without overwhelming complexity.
2Measurement precision
If search systems use limited information at early stages of query analysis, then the processing time is reduced, but the intent detection accuracy deteriorates due to insufficient context
Solution Approach 1:
The patent applies preliminary action by generating multiple semantic hypotheses early in the query analysis process, before full context is processed. This allows the system to prepare multiple potential interpretations in advance, then refine and select from these pre-generated hypotheses as more context becomes available, thereby improving intent detection accuracy without requiring sequential processing of all information.
Solution Approach 2:
The patent introduces dynamics to the query analysis process by making the hypothesis generation and selection process adaptive. The system dynamically adjusts the depth and extent of analysis based on the complexity of the query and the amount of available context, allowing for accurate intent detection while optimizing processing time through flexible, context-aware analysis depth.
3Adaptability or versatility
If search systems create only one semantic hypothesis, then the processing simplicity is maintained, but the adaptability to different query interpretations is reduced
Solution Approach 1:
The patent applies universality by creating a multi-functional semantic analysis system that can handle multiple query interpretations simultaneously. The same core analysis infrastructure is enhanced to generate and evaluate multiple hypotheses, allowing the system to adapt to different query meanings without requiring separate specialized processing paths for each interpretation type.
Data Source
AI summary
Examples of the present disclosure describe systems and methods for generating multiple semantic hypotheses for search query intent understanding. In aspects, a search query may be received by a query analysis component associated with a search system. The query analysis component may be used to evaluate the search query for ambiguity in the domain, intent, and/or slot(s) of the search query. A set of hypotheses representing for one or more combinations of the domain, intent, and/or slot(s) of the search query may be generated. The set of hypotheses may be scored and/or ranked. Based on the scores/ranks, one or more of the hypotheses in the set of hypotheses may be provided to a user and/or one or more processing components accessible to the search system.