Boosted LDA With Predefined Clusters and Repelling Force
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional Latent Dirichlet Allocation (LDA) for topic modeling faces challenges in determining the number of topics, interpreting abstract or multidimensional topics, and capturing nuances like sentiment or sarcasm due to its bag-of-words model that ignores word order and context.
Innovation Solution
Implementing a boosted LDA model with predefined clusters and a repelling force, where seed words are used to pre-populate clusters, and a repelling force is applied during training to improve clustering accuracy, especially for similar topics, and enable classification and explainability of user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional LDA is used for topic modeling, then topics can be extracted from large text collections, but determining the number of topics is difficult and topics may overlap or be redundant
Solution Approach 1:
The patent applies preliminary action by pre-defining cluster structures and seed word assignments before the main topic modeling process. This provides initial guidance to the algorithm, helping it converge to meaningful topic structures without requiring precise pre-specification of the number of topics, thus resolving the contradiction between automation and precision in topic number determination.
2Loss of information
If conventional LDA extracts more topics, then more information is captured, but topics become overlapping and redundant
Solution Approach 1:
The patent applies segmentation by dividing the topic space into pre-defined clusters with distinct themes. Each cluster contains seed words that define its boundaries, ensuring that even when multiple topics are extracted, they remain distinct and non-redundant. This segmentation approach allows capturing comprehensive information while maintaining topic reliability and distinctiveness.
3Device complexity
If conventional LDA uses bag-of-words model, then processing is simplified, but word order and context nuances like sentiment are lost
Solution Approach 1:
The patent introduces cluster assignments and seed word associations as intermediary structures between the simple bag-of-words input and the final topic interpretation. These intermediaries preserve contextual information and semantic relationships without significantly increasing model complexity, allowing the system to capture sentiment and context while maintaining computational efficiency.
4Adaptability or versatility
If conventional LDA assigns topics probabilistically, then flexibility is maintained, but interpretability and meaningful labeling of topics becomes difficult
Solution Approach 1:
The patent applies self-service by having the system automatically generate interpretable topic labels and explanations based on the cluster assignments and seed word associations. This self-labeling capability maintains the flexibility of probabilistic topic assignment while automatically providing the interpretability needed for meaningful topic understanding, eliminating the need for manual labeling efforts.
Data Source
AI summary
Systems and methods for a boosted Latent Dirichlet Allocation model with predefined clusters and repelling force. Herein, clusters are pre-populated with seed words with amplified weights to predefine the characteristics of clusters. A repel force is also applied while assigning words to specific clusters in order to assign words more accurately to clusters in instances where clusters have similar characteristics. In addition to generating improved clustering of words, systems and methods may analyze words within queries to provide explainability output related to the performance of content or data.