Selective Topic Model Fusion for Document Analysis
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Solution Overview
Problem
Existing topic modeling systems rely on a single fixed model, which can lead to loss of qualified topics and incorrect decisions, and combining multiple algorithms is resource-intensive and difficult to manage, lacking an efficient method for selecting the best model for effective insights.
Innovation Solution
A system that applies multiple topic models to documents, uses a primary and secondary topic selection mechanism to fuse models, and generates holistic topic sets, allowing for the automatic selection of primary and secondary topics based on similarity scores and cluster comparison, thereby capturing all relevant topics without redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single fixed topic model is used to determine document topics, then the system is simple and easy to manage, but it may lose qualified topics and cause wrong decisions
Solution Approach 1:
The patent combines multiple topic modeling algorithms (e.g., LDA, NMF, HDBSCAN) into a unified system that processes documents through all algorithms and integrates their results. This merging approach ensures comprehensive topic coverage by leveraging the strengths of different algorithms while maintaining system manageability through automated selection mechanisms.
Solution Approach 2:
The system dynamically selects which topic models to apply and how to combine their results based on the specific characteristics of the input documents. The automated selection process adapts the modeling approach to match document properties, transforming the static single-model approach into a dynamic multi-model system that optimizes topic determination accuracy for different scenarios.
2Reliability
If multiple topic modeling algorithms are combined to improve topic capture, then topic determination accuracy improves, but it becomes time and resource consuming
Solution Approach 1:
The system performs preliminary analysis of document characteristics before applying topic models, and uses pre-established selection criteria to determine which algorithms to apply. This preliminary action prevents unnecessary computation by selecting only the most suitable algorithms for each document type, thereby maintaining high accuracy while improving processing efficiency.
Solution Approach 2:
The system changes parameters such as the number of topics, dimensionality reduction settings, and algorithm selection based on document properties like length, domain, and complexity. By adapting these parameters dynamically, the system optimizes the balance between comprehensive topic capture and processing efficiency for different document types.
3Loss of information
If multiple topic modeling algorithms are combined to capture all topics, then topic completeness improves, but human effort to tune and find the best combination increases
Solution Approach 1:
The system performs self-service by automatically selecting and tuning the appropriate topic modeling algorithms based on document characteristics. The automated selection process eliminates the need for manual intervention to determine the best algorithm combination, while still achieving comprehensive topic coverage through the coordinated application of multiple algorithms.
Solution Approach 2:
The system incorporates feedback mechanisms that evaluate the performance of different algorithm combinations on document data and automatically adjust the selection and parameters accordingly. This feedback-driven approach ensures complete topic coverage while minimizing manual tuning effort, as the system learns from previous results to optimize future topic modeling.
Data Source
AI summary
Systems and methods for determining topics for a document based on selective topic models are disclosed. In some embodiments, a disclosed method includes: obtaining at least one document; applying a plurality of topic models to the at least one document to identify at least one topic associated with the at least one document; selecting a topic model from the plurality of topic models based on the at least one topic; generating topic related data comprising data associated with a topic identified based on the selected topic model; and storing the topic related data in a database.