Anaphora Resolution via Contextual Data Matching
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Solution Overview
Problem
Conventional anaphora resolution techniques in computer-based systems rely primarily on speech analysis and fail to effectively incorporate contextual data such as user positioning, orientation, and object metadata, leading to reduced accuracy in identifying the object referred to by anaphoras.
Innovation Solution
A method that extracts individual context data from user expressions, determines if it includes an anaphora representation, and matches it with object data using contextual information like user positioning, orientation, and object metadata stored in a database, improving accuracy by considering environmental and user context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional speech analysis is used for anaphora resolution, then the system is simple to operate, but the accuracy of identifying the referred object is reduced
Solution Approach 1:
The patent combines multiple data sources including speech analysis, user positioning data, orientation data, and object metadata into a unified anaphora resolution system. This merging of diverse contextual information sources enables more accurate object identification while managing system complexity through integrated processing.
Solution Approach 2:
The patent extends traditional speech-based anaphora resolution by adding spatial and contextual dimensions. It incorporates user positioning, orientation, and environmental metadata to create a multi-dimensional approach that significantly improves accuracy beyond conventional single-dimension speech analysis.
2Measurement precision
If additional contextual data is collected for anaphora resolution, then the accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent segments the complex task of anaphora resolution into distinct processing components: extracting individual context data from expressions, determining anaphora representations, extracting anaphora context data, and comparing with object data. This segmentation makes the overall complex process more manageable and systematic.
Solution Approach 2:
The system performs preliminary actions by collecting and storing object metadata, user positioning information, and orientation data before anaphora resolution is needed. This pre-processing of contextual data reduces the complexity of real-time processing when anaphora resolution is actually required.
Data Source
AI summary
A method, a structure, and a computer system for resolving an anaphora. The exemplary embodiments may include extracting individual context data from an individual expression and determining whether the individual expression includes an anaphora representation based on the individual context data. The exemplary embodiments may further include, based on determining that the individual expression includes the anaphora representation, extracting anaphora context data and identifying an object of one or more objects to which the anaphora representation refers based on comparing the individual context data and the anaphora context data to data detailing the one or more objects.