Statistical Model Translates Natural Language Queries to Keyword Search
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Solution Overview
Problem
Conventional approaches to handling keyword search queries fail to improve search results for natural language queries due to the significant variation in conversational style between users, even when attempting to create the same search parameters.
Innovation Solution
A statistical model is trained using query click log data to detect domains and translate natural language queries into keyword search queries, leveraging domain-independent salient phrases and statistical machine translation to improve domain detection and search result relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional approaches use search engine logs to determine query-link correlations, then keyword search queries can be handled effectively, but natural language queries fail to improve search results due to variation in conversational style
Solution Approach 1:
The patent introduces a statistical machine translation model as an intermediary between natural language queries and keyword search queries. This mediator translates the varied conversational styles of natural language into standardized keyword queries that the search engine can effectively process, thereby maintaining search accuracy while adapting to natural language variation
Solution Approach 2:
The system changes the parameter representation of queries by transforming natural language queries into keyword queries through statistical translation. This parameter transformation allows the search engine to process diverse natural language inputs using its existing keyword-based optimization, resolving the contradiction between reliability and adaptability
2Reliability
If the system translates natural language queries into keyword search queries, then search result relevance improves, but the complexity of the system increases due to domain detection and translation components
Solution Approach 1:
The patent segments the query processing system into distinct functional components: a domain detection module that identifies the topic area of the query, and a statistical translation module that converts the query to keyword form. This segmentation allows each component to specialize in one task, improving overall effectiveness while making the complexity manageable through modular design
Solution Approach 2:
The statistical translation model serves multiple functions: it performs domain-specific translation, adapts to different conversational styles, and maintains compatibility with existing keyword-based search infrastructure. This multi-functionality justifies the added complexity by providing comprehensive benefits across multiple dimensions
Data Source
AI summary
Natural language query translation may be provided. A statistical model may be trained to detect domains according to a plurality of query click log data. Upon receiving a natural language query, the statistical model may be used to translate the natural language query into an action. The action may then be performed and at least one result associated with performing the action may be provided.