Query Name Detection via Object-Verb Analysis
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Solution Overview
Problem
Existing information retrieval systems on user devices, such as mobile phones, often fail to accurately determine whether a query seeks a person's name, leading to incorrect answers being provided or missed relevant names in responses.
Innovation Solution
A method that identifies objects and verbs in queries, generates a confidence score based on the occurrence of related terms in documents, and adjusts document scores to prioritize or deprioritize results associated with names, ensuring accurate identification and output of names relevant to queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing information retrieval systems provide answers to queries, then users receive responses, but the accuracy of determining whether a query seeks a person's name deteriorates leading to incorrect answers
Solution Approach 1:
The system performs preliminary analysis of query structure by identifying objects and verbs before generating answers. It calculates a confidence score in advance to determine whether the query seeks a person's name, preventing incorrect responses before they occur.
Solution Approach 2:
The system changes the parameter of document scoring by adjusting scores based on name association. Documents associated with names receive score adjustments, allowing the system to differentiate between queries seeking names versus other information types.
2Measurement precision
If the system generates detailed confidence scores based on multiple factors, then the accuracy of name-triggering query identification improves, but the computational complexity increases
Solution Approach 1:
The confidence score calculation is segmented into distinct components: object occurrence frequency, related term occurrence frequency, and document score adjustments. This modular approach allows precise measurement while managing computational complexity through structured processing.
3Reliability
If the system adjusts document scores to prioritize name-associated results, then the relevance of returned documents improves, but the processing time increases
Solution Approach 1:
The system applies local quality adjustments by modifying document scores only for documents associated with names, rather than reprocessing all documents uniformly. This targeted approach maintains high relevance while minimizing additional processing time.
Data Source
AI summary
A system may be configured to receive a query; identify an object and a verb associated with the query; obtain information indicating a quantity of occurrences, in one or more documents, of the object in a sentence that may include the verb, or a quantity of occurrences, in one or more documents, of one or more terms, which are related to the object, in a sentence that may include the verb; generate a confidence score that may indicate a probability that the query seeks a result relating to a name of a person; identify a set of documents that are responsive to the query; determine that the confidence score satisfies a threshold; identify one or more documents, of the set of documents, that are associated with one or more names of people; select a particular document, of the set of documents; and output information regarding the selected particular document.