Corpus Link Model for Real-Time Chat Search
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Solution Overview
Problem
Existing collaborative communication applications face challenges in generating accurate and relevant search terms due to reliance on generalized probabilistic models, leading to ambiguity and irrelevant results, especially in real-time chat environments where user-specific linguistic tendencies are not considered.
Innovation Solution
A computer-implemented method that trains a corpus link model based on linguistic analysis and author metrics to generate collocated terms, providing a co-occurrence rating for optimal term presentation in collaborative chat searches and content management systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generalized probabilistic models are used for search term generation, then the system can handle diverse queries, but the search results become ambiguous and irrelevant due to inability to capture user-specific linguistic patterns
Solution Approach 1:
The system segments search term generation into multiple components: a corpus link model trained on specific linguistic corpora, author metrics analysis, and collocation generation. This segmentation allows the system to handle diverse queries through the trained model while maintaining precision through user-specific linguistic pattern recognition.
Solution Approach 2:
The system performs preliminary training of the corpus link model on specific linguistic corpora and author metrics before actual search operations. This preliminary action captures user-specific linguistic patterns in advance, enabling the system to generate relevant search terms without sacrificing precision when handling diverse queries.
2Ease of operation
If traditional hierarchical structures are used for disambiguation, then the system can organize search results, but performance degrades when generalizing outside initial communication scope
Solution Approach 1:
The system replaces static hierarchical structures with a dynamic corpus link model that adapts to different linguistic contexts. The model dynamically generates collocated terms based on trained linguistic patterns and author metrics, maintaining ease of operation through automated generation while achieving versatility by adapting to various communication scopes.
Solution Approach 2:
The system changes the parameters of term generation by using trained corpus-specific models instead of fixed hierarchical rules. By adjusting the model based on different linguistic corpora and author metrics, the system maintains organized result presentation while improving generalization capability across different communication contexts.
3Productivity
If fixed hierarchical structures and filtering are used for search, then the system can process queries efficiently, but it produces vague collocated terms that impede effective communication
Solution Approach 1:
The system enables self-service by automatically generating accurate collocated terms through the trained corpus link model without relying on fixed hierarchical filtering. The model autonomously processes queries efficiently while producing reliable, context-specific collocations based on learned linguistic patterns and author metrics.
Solution Approach 2:
The system replaces the mechanical filtering approach based on fixed hierarchical structures with a learned model approach. The corpus link model, trained on specific linguistic corpora, substitutes the rigid mechanical filtering process, maintaining query processing efficiency while significantly improving collocation accuracy and relevance.
Data Source
AI summary
In an approach to training a corpus link model and generating collocated terms for intra-channel and inter-channel activity, one or more computer processors train a corpus link model based on an analysis of a linguistic corpus and an analysis of one or more author metrics. The one or more computer processors generate one or more collocated terms based on one or more calculations by the trained corpus link model. The one or more computer processors generate a co-occurrence rating for each of the one or more generated collocated terms. The one or more computer processors display the one or more generated collocated terms according to the generated co-occurrence rating of each collocated term.