Name Disambiguation via Segmented Processing
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Solution Overview
Problem
The challenge lies in disambiguating personal and organization names shared among multiple individuals and entities, as existing systems struggle to accurately determine which specific person or organization is being referenced in electronic content items, especially when names are similar or have varying forms.
Innovation Solution
A system and method that categorize names as famous or non-famous, and further categorize non-famous names as common or uncommon, using machine-learning models to disambiguate occurrences based on feature vectors assembled from content items, including affiliations, popularity scores, and contextual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If name disambiguation is performed for all names including common names, then identification accuracy improves, but system complexity and processing time increase
Solution Approach 1:
The system segments names into different categories (famous vs. non-famous/common) and applies different disambiguation strategies to each category. Famous names receive full disambiguation processing while common names use simplified routing, reducing overall system complexity while maintaining accuracy for names that require it
Solution Approach 2:
Different levels of disambiguation processing are applied to different names based on their category. Famous names receive comprehensive analysis with multiple features, while common names receive streamlined processing, optimizing resource allocation and reducing unnecessary complexity
2Measurement precision
If comprehensive feature vectors are assembled for all names, then disambiguation precision improves, but processing time and computational resources increase
Solution Approach 1:
The system divides name processing into segments based on name category. Famous names undergo comprehensive feature vector assembly and model-based disambiguation, while common names use simplified feature sets and direct routing, reducing overall processing time while maintaining precision where needed
Solution Approach 2:
The system applies partial disambiguation action to common names by using simplified features and direct routing instead of full comprehensive analysis. This partial action is sufficient for common names while saving computational resources and time
3Reliability
If all name variations and forms are considered, then identification completeness improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and categorizing names into famous and non-famous groups before disambiguation. This preliminary classification simplifies subsequent matching operations by establishing clear processing paths for different name types
Solution Approach 2:
The system introduces an intermediary classification layer (famous vs. common name categorization) that mediates between raw name input and disambiguation processing. This intermediary step organizes name variations systematically, making detection and measurement more manageable
Data Source
AI summary
A system, method, and apparatus are provided for disambiguating personal names. Selected names that are shared among multiple people are categorized or characterized as either famous or non-famous and, if non-famous, are also designated as common or uncommon. As content items are received (e.g., news stories, magazine articles, social media content), occurrences of the selected names are identified. Each item that includes at least one name is then processed to determine which of the multiple people that have the name (if any) is the person referenced in the item. Depending on the name's categorization (e.g., famous, non-famous and common, non-famous and uncommon), different models or procedures may be applied to disambiguate the occurrence of the name.