Dynamic Content Tagging Using LDA and Contextual Embeddings
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Solution Overview
Problem
Existing content tagging systems rely heavily on manual tagging and limited training data, leading to inaccurate and inefficient categorization, especially in dynamic environments where content changes rapidly.
Innovation Solution
A system that utilizes a latent Dirichlet allocation (LDA) model to generate data-dependent content tags, supplemented by deep learning models, to automatically categorize content with improved accuracy and reduced reliance on manual tagging, using contextual N-grams to represent coherent phrases and probabilistic graph models for topic clustering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging is used to categorize content, then tag accuracy can be maintained through human judgment, but productivity decreases due to the time-consuming nature of manual categorization
Solution Approach 1:
The system performs preliminary automated tagging using LDA models and deep learning models before final tag selection. These models pre-process content and generate candidate tags, which are then refined through validation mechanisms, reducing the need for complete manual tagging while maintaining accuracy
Solution Approach 2:
The patent introduces automated tagging models (LDA and deep learning models) as intermediaries between raw content and final tags. These models act as mediators that process content automatically while their outputs can be validated and refined, combining automated efficiency with quality control
2Device complexity
If limited training data is used for content tagging models, then the system complexity decreases, but measurement precision of tag prediction deteriorates
Solution Approach 1:
The patent combines multiple tagging approaches (LDA models and deep learning models) into a unified system. By merging these different model types, the system leverages their complementary strengths to achieve better prediction accuracy than either model could provide alone, while managing complexity through integrated architecture
Solution Approach 2:
The system uses a composite modeling approach, combining probabilistic LDA models with neural network-based deep learning models. This composite structure allows the system to benefit from both generative modeling capabilities and pattern recognition strengths, improving tag prediction accuracy without requiring excessively large training datasets
3Stability of the object's composition
If traditional tagging systems are used in dynamic environments, then system stability is maintained, but adaptability to rapidly changing content decreases
Solution Approach 1:
The patent implements dynamic tagging by training LDA and deep learning models on current content data, allowing the system to adapt to changing content characteristics. The models can be retrained as new content emerges, enabling the system to maintain stability through consistent methodology while adapting to new patterns and topics
Data Source
AI summary
Dynamic content tags are generated as content is received by a dynamic content tagging system. A natural language processor (NLP) tokenizes the content and extracts contextual N-grams based on local or global context for the tokens in each document in the content. The contextual N-grams are used as input to a generative model that computes a weighted vector of likelihood values that each contextual N-gram corresponds to one of a set of unlabeled topics. A tag is generated for each unlabeled topic comprising the contextual N-gram having a highest likelihood to correspond to that unlabeled topic. Topic-based deep learning models having tag predictions below a threshold confidence level are retrained using the generated tags, and the retrained topic-based deep learning models dynamically tag the content.