Multi-model NLP for Dynamic Skill Catalog Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing natural language processing technologies face challenges in accurately predicting skill matches between job seekers and job listings due to the use of static language models that fail to account for dynamic changes in language and context, leading to poor performance and excessive resource utilization.
Innovation Solution
A multi-model natural language processing approach that generates multiple language models and skill catalogs based on diverse data sources and processing techniques, allowing for dynamic selection of the most appropriate model and catalog for specific predictions, thereby improving accuracy and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a static language model is used for natural language processing, then the system complexity is reduced, but the prediction accuracy deteriorates due to inability to account for dynamic changes in language and context
Solution Approach 1:
The patent applies the dynamics principle by transitioning from a static language model to a dynamic multi-model system. The system generates multiple language models from diverse data sources and dynamically selects the most appropriate model based on the specific prediction context. This allows the system to adapt to changing language patterns and contexts, thereby improving prediction accuracy while maintaining manageable complexity through automated model selection.
Solution Approach 2:
The patent implements parameter changes by varying the language models based on different data sources and processing techniques. By generating multiple language models with different parameters and characteristics from diverse data sources, the system can select the model whose parameters best match the current context, thus improving prediction accuracy without requiring a single overly complex model.
2Measurement precision
If multiple language models and skill catalogs are generated and maintained, then the prediction accuracy is improved through dynamic selection, but the device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the single language model into multiple specialized language models, each generated from different data sources and optimized for specific contexts. Similarly, skill catalogs are segmented and associated with specific language models. This segmentation allows the system to select only the relevant model and catalog for each prediction, improving accuracy while managing complexity through modular organization.
Solution Approach 2:
The system implements self-service through automated model and catalog selection. Rather than requiring manual intervention to choose the appropriate language model and skill catalog, the system automatically evaluates the context and selects the most suitable combination, thereby managing the complexity of multiple models and catalogs without proportionally increasing operational complexity.
3Ease of operation
If a single language model is used for all predictions, then the ease of operation is maintained, but the adaptability deteriorates due to inability to account for different contexts
Solution Approach 1:
The patent implements universality by creating a multi-functional system where multiple language models and skill catalogs serve different contextual requirements. The system universally handles various prediction scenarios by selecting the appropriate model-catalog pair, thereby maintaining ease of operation (single interface for all predictions) while achieving high adaptability to different contexts through the diverse model ensemble.
Data Source
AI summary
In some implementations, a device may monitor a set of data sources to generate a set of language models corresponding to the set of data sources. The device may determine a plurality of sets of keyword groups. The device may generate a plurality of sets of skill catalogs. The device may receive a source document for processing. The device may process the source document to extract a key phrase set and to determine a first similarity distance. The device may select a corresponding skill catalog and an associated language model based on a relevancy value. The device may determine second similarity distances between the source document and one or more target documents using the corresponding skill catalog and the associated language model. The device may output information associated with one or more target documents based at least in part on the second similarity distances.