Query Entity Identification via Dependency Tree Segmentation
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Solution Overview
Problem
Computing systems face challenges in efficiently identifying and responding to user queries that seek entities, often providing irrelevant results or requiring extensive searches across multiple types of entities.
Innovation Solution
A system that maps user queries to dependency trees to determine the type of entity sought, identifies relevant entities, and provides responses based on these determinations, while also adapting to user behavior and preferences by initiating dialogs for additional details and managing data privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system performs extensive searches across multiple types of entities to ensure comprehensive results, then the completeness of entity identification is improved, but the query resolution speed deteriorates
Solution Approach 1:
The system segments the entity search process by first identifying the entity type through dependency tree analysis, then focusing the search on that specific type. This segmentation allows the system to maintain comprehensive search coverage for the relevant entity type while avoiding unnecessary searches across all entity types, thus resolving the contradiction between completeness and speed.
Solution Approach 2:
The system performs preliminary analysis of the query using dependency tree parsing to determine the entity type before executing the full entity search. This preliminary action filters the search space in advance, enabling the system to achieve complete entity identification for the relevant type without the time cost of searching across all possible entity types.
2Reliability
If the system searches across all possible entity types to ensure no relevant entity is missed, then the coverage of search results is improved, but the computational resources required deteriorate
Solution Approach 1:
The system segments the computational effort by dividing the search process into two stages: entity type identification through dependency tree analysis, and targeted entity search within that type. This segmentation reduces computational resources by eliminating the need to search across all entity types while maintaining complete coverage for the relevant type.
Solution Approach 2:
The system extracts the entity type information from the query through dependency tree parsing, separating this identification function from the entity search function. This extraction allows the system to focus computational resources only on searching for entities of the identified type, reducing overall computational resource requirements while maintaining search coverage.
3Loss of information
If the system provides detailed search results listings for entity queries, then the information completeness is improved, but the user experience for direct entity identification deteriorates
Solution Approach 1:
The system applies partial action by providing only the necessary entity identification information when the query seeks a specific entity, rather than providing complete search result listings. This partial action delivers sufficient information for direct entity identification without the excess of comprehensive search results, improving user experience while maintaining information completeness for the identified entity.
Data Source
AI summary
In some implementations, a query that includes a sequence of terms is obtained, the query is mapped, based on the sequence of the terms, to a dependency tree that represents dependencies among the terms in the query, an entity type that corresponds to an entity sought by the query is determined based on a term represented by a root of the dependency tree, a particular entity is identified based on both the entity type and a relevance of the entity to the terms in the query, and a response to the query is provided based on the particular entity that is identified.