Hierarchical Topic Model for Rare Topic Detection
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Solution Overview
Problem
Current methods for topic detection in large volumes of text-based data, such as real-time communication messages, face challenges in efficiently identifying rare topics due to computational costs and the failure of distance metrics when dominant words are not removed from vectors, especially in hierarchical agglomerative operations.
Innovation Solution
The implementation of hierarchical topic modeling by a processor, which learns from data sources, iteratively removes dominant words related to primary topics, seeds the model with words, n-grams, or phrases, and reinstates them for enhanced readability and interpretability, allowing for progressive drilldown and incremental training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hierarchical agglomerative operations are performed on text vectors, then topic detection capability is improved, but computational cost increases and distance metrics fail when dominant words are not removed
Solution Approach 1:
The patent extracts and removes dominant words from text vectors before performing hierarchical agglomerative operations. This extraction process eliminates the harmful effect of dominant words that cause distance metrics to fail, while enabling the topic detection algorithm to operate effectively on the remaining meaningful terms.
Solution Approach 2:
The patent segments the text analysis process into distinct stages: (1) identifying and removing dominant words, (2) performing hierarchical agglomerative operations on the cleaned vectors, and (3) optionally reinstating dominant words for interpretability. This segmentation allows each stage to be optimized independently, reducing overall computational complexity.
2Measurement precision
If dominant words are removed from vectors to enable distance metrics, then rare topic detection is improved, but interpretability and readability decrease
Solution Approach 1:
The patent temporarily discards dominant words from the vectors during the hierarchical agglomerative operations to enable rare topic detection, then recovers and reinstates them after the clustering is complete. This allows the algorithm to benefit from their removal during processing while preserving their contribution to interpretability and readability in the final results.
Solution Approach 2:
The patent performs the removal of dominant words as a preliminary action before the main topic detection algorithm executes. This preliminary processing step prepares the data in a form suitable for rare topic detection without permanently losing the dominant words, as they can be restored afterward for explanation purposes.
3Quantity of substance
If hierarchical topic modeling is applied to large volumes of text data, then analysis comprehensiveness is improved, but processing time increases
Solution Approach 1:
The patent extracts and removes dominant words from large volumes of text data before processing, which reduces the dimensionality and complexity of the vectors. This extraction reduces the computational burden of hierarchical agglomerative operations, enabling the system to handle large data volumes more efficiently while maintaining comprehensive analysis coverage.
Data Source
AI summary
Embodiments for providing rare topic detection using hierarchical topic modeling by a processor. A hierarchical topic model may be learned from one or more data sources. One or more dominant words in a selected cluster may be iteratively removed using the hierarchical topic model. The dominant words may relate to one or more primary topics of the cluster. The learned hierarchical topic model may be seeded with one or more words, n-grams, phrases, text snippets, or a combination thereof to evolve the hierarchical topic model and the removed dominant words are reinstated upon completion of the seeding.