Language Data Labeling with Topic Embeddings and Semantic Clustering
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Solution Overview
Problem
Managing and organizing large numbers of language data structures stored on a computing system is challenging, as existing methods lack effective ways to label and present them efficiently.
Innovation Solution
A method utilizing a combination of a large language model and a clustering model to assign topics to language data structures, generate vector data structures, and cluster them based on semantic similarity, allowing for efficient organization and labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual organization and labeling methods are used, then labeling precision can be maintained, but productivity and time efficiency deteriorate significantly
Solution Approach 1:
The system enables self-service through automated topic assignment where the language model independently analyzes datasets and assigns topics without human intervention. The clustering model automatically groups datasets based on semantic similarity, allowing the system to organize and label data structures autonomously, thereby dramatically improving productivity while maintaining acceptable labeling accuracy through sophisticated algorithms
Solution Approach 2:
The patent replaces manual mechanical organization processes with automated computational systems. The language model substitutes human linguistic analysis, while the clustering model replaces manual categorization. This substitution of mechanical human operations with automated computational mechanisms enables high-speed processing of large datasets while maintaining organization quality through intelligent algorithms
2Quantity of substance
If more language data structures are stored, then quantity of information increases, but ease of operation and management deteriorates
Solution Approach 1:
The system segments the large collection of language data structures into manageable clusters based on semantic similarity. By dividing the extensive dataset into smaller, coherent groups (clusters), each containing related data structures, the system makes management easier while preserving the overall quantity of information. Users can navigate and manage data through these organized segments rather than facing a monolithic large dataset
Solution Approach 2:
The patent introduces topic assignments and clusters as intermediary layers between the raw data structures and the user. These intermediaries serve as organizing principles that mediate the relationship between users and large volumes of data, making the data more manageable and operable. The topics and clusters act as intermediaries that structure and simplify access to extensive datasets
3Productivity
If existing labeling methods are used, then device complexity remains low, but productivity and organization efficiency deteriorate
Solution Approach 1:
The system achieves multi-functionality by employing a unified approach that combines language modeling, encoding, and clustering capabilities within a single integrated system. The language model handles topic generation, the encoding model transforms topics into vector representations, and the clustering model organizes datasets - all within one cohesive system. This universal approach improves organization efficiency while managing complexity through functional integration rather than separate systems
Data Source
AI summary
A method including applying a language model to datasets to generate topics assigned to the datasets. Each of the topics includes at least one of a natural language text word and a natural language phrase. The method also includes applying an encoding model to the topics to generate a corresponding vector data structures storing embedded topics. Each embedded topic of the embedded topics is associated with one corresponding vector in the vector data structures. The method also includes applying a clustering model to the vector data structures to generate a cluster including a subset of the vector data structures. The subset includes a reduced number of the vector data structures. The method also includes modifying, according to the cluster, the datasets.