Social Context Topic Inference for Ambiguous Anchor Terms
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Solution Overview
Problem
Social networking systems face challenges in accurately correlating user communications with specific topics due to plain text communications not being manually associated with subjects, leading to ambiguous word meanings and limited functionality in displaying these correlations.
Innovation Solution
The system identifies an anchor term in user communications, uses a dictionary to find candidate nodes representing possible meanings, and determines the context to score these nodes, selecting the most likely meaning based on social context and user interactions, while also prompting users to select intended topics during typing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated topic recognition is used to determine the meaning of words in communications, then the system can automatically correlate communications with topics without manual input, but the meaning of ambiguous words may be determined incorrectly
Solution Approach 1:
The patent introduces an intermediary process that combines automated topic recognition with social context analysis. The system uses multiple intermediate steps: identifying candidate topics through automated recognition, then filtering and scoring these candidates based on social context from the user's network. This intermediary layer between pure automation and final topic assignment resolves the contradiction by maintaining automation while improving precision through contextual verification.
Solution Approach 2:
The system implements feedback loops where topic inference results are continuously refined based on social context information. The patent describes processes where candidate topics are scored and re-ranked based on feedback from analyzing communications within the user's social network. This feedback mechanism allows the system to learn from contextual patterns and improve the accuracy of ambiguous word interpretation while maintaining automated operation.
2Ease of operation
If plain text communications are used without manual subject association, then ease of communication is improved, but the ability to correlate communications with particular subjects is limited
Solution Approach 1:
The patent applies preliminary action by pre-building a social context database that contains topic correlations from communications within the user's social network before the actual topic inference is needed. The system预先 analyzes and stores contextual patterns, user interests, and topic associations from the social graph. When a communication needs topic correlation, this pre-computed social context is quickly applied, maintaining ease of communication while recovering subject correlation information that would otherwise be lost in plain text.
3Measurement precision
If social context analysis is performed to improve topic inference accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex topic inference process into distinct modular components: candidate topic identification, social context retrieval, scoring function application, and final topic selection. Each module handles a specific aspect of the analysis independently. The social graph is also segmented into relevant subsets (user's friends, groups, interests) that are queried selectively. This segmentation reduces overall system complexity by making each component manageable and independently optimizable while maintaining high inference accuracy through the coordinated operation of all segments.
Data Source
AI summary
A social networking system determines the meaning of an anchor term used in a communication received from a communicating user. Candidate nodes are identified in the dictionary based on the anchor term, where each candidate node represents a possible meaning of the anchor term. The context of the anchor term is determined, and a score is determined for each candidate node based on the determined context. A candidate node is selected that most likely represents the meaning of the anchor term based on the determined candidate node scores. The context of the anchor term may be a social context derived from users connected to the communicating user that use the anchor term in communications. A communicating user may be prompted to identify the meaning of the anchor term explicitly based on the use of the term in communications from other users connected to the communicating user.