Contextual N-Gram Tagging With LDA for Dynamic Content Classification
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Solution Overview
Problem
Existing content tagging systems rely on manual tagging and limited training data, leading to inaccurate and inefficient categorization, especially when adapting to dynamic content changes.
Innovation Solution
A system that utilizes a latent Dirichlet allocation (LDA) model to generate data-dependent content tags, supplemented by deep learning models, to enhance automated content categorization and reduce reliance on manual tagging, by generating contextual N-grams and using Bayesian inference for probabilistic clustering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging is used for content categorization, then tag accuracy can be maintained through human judgment, but productivity is reduced due to the time-consuming nature of manual processes
Solution Approach 1:
The system performs preliminary automated tagging using LDA models to generate candidate tags before final selection, preparing the content in advance for more accurate and efficient categorization without requiring complete manual tagging from scratch
Solution Approach 2:
The patent introduces an intermediary automated tagging system that bridges manual tagging accuracy and automated processing speed, using LDA-generated tags as intermediate results that can be reviewed or directly applied based on confidence thresholds
2Device complexity
If limited training data is used for content tagging models, then the system complexity is reduced, but measurement precision deteriorates due to insufficient learning samples
Solution Approach 1:
The LDA model performs self-service by automatically generating topic tags from the content itself without requiring external labeled training data, enabling the system to create its own training signals and improve accuracy while maintaining low complexity
Solution Approach 2:
The system changes the parameter of training data requirements by shifting from supervised learning (requiring labeled data) to unsupervised topic modeling (requiring only raw text), thereby maintaining accuracy while reducing data requirements
3Ease of operation
If generic tagging methods are used for content categorization, then ease of operation is improved through standardized processes, but adaptability deteriorates when facing dynamic content changes
Solution Approach 1:
The system implements dynamic adaptability by continuously applying LDA topic modeling to new content, allowing the tagging system to automatically adjust to changing content patterns while maintaining a simple automated operation process
Solution Approach 2:
The LDA-based tagging system serves multiple functions simultaneously: it categorizes content, discovers topics, adapts to new content types, and provides interpretable tags, thereby achieving both ease of operation and adaptability through a single unified approach
Data Source
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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.