Domain-Aware Sentence Embeddings via Similarity Models
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Solution Overview
Problem
Existing natural language processing solutions face inefficiencies and reliability issues in performing predictive data analysis operations, particularly in integrating domain-specific insights and handling the absence of ground-truth data for training domain-aware sentence embeddings.
Innovation Solution
The use of sentence embedding machine learning models trained in coordination with similarity determination models, leveraging knowledgebase graph data objects to generate domain-aware sentence embeddings, and integrating these embeddings into machine learning frameworks for improved predictive accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sentence embedding models are trained using traditional supervised learning methods, then model training is straightforward, but the system cannot generate domain-aware embeddings without ground-truth data
Solution Approach 1:
The patent introduces a similarity determination model as an intermediary component that bridges the sentence embedding model and ground-truth data. This mediator computes similarity scores between sentence pairs, enabling the system to leverage domain-specific knowledge from knowledgebase graphs without requiring direct ground-truth labels for embedding training. The intermediary transforms the problem from direct supervised learning to indirect learning through similarity signals.
Solution Approach 2:
The system performs preliminary actions by pre-computing similarity scores between sentence pairs using the similarity determination model before training the sentence embedding model. These pre-computed similarity scores are then used as training targets, allowing the embedding model to learn domain-aware representations in advance without requiring ground-truth data during the actual training process.
2Adaptability or versatility
If domain-specific knowledge is integrated into the training process, then domain-aware embeddings are achieved, but the training process becomes more complex
Solution Approach 1:
The patent segments the training process into distinct modular components: (1) sentence pair generation from domain data, (2) similarity score computation using a separate determination model, (3) embedding model training using these similarity scores as targets. This segmentation allows each component to be developed and optimized independently, reducing overall system complexity while achieving domain-aware embeddings.
Solution Approach 2:
The similarity determination model serves multiple functions: it computes similarity scores for training the embedding model, validates embedding quality, and can be applied across different domains. This multi-functionality reduces the need for domain-specific customization and simplifies the overall training process while maintaining domain awareness.
3Measurement precision
If manual annotation of ground-truth data is performed, then training data quality is high, but time consumption and cost increase significantly
Solution Approach 1:
The system implements self-service by automatically generating training data through the similarity determination model without requiring manual annotation. The model computes similarity scores between sentence pairs automatically, using domain-specific knowledge from knowledgebase graphs to guide the process. This eliminates the need for time-consuming manual labeling while maintaining high data quality through the model's inherent understanding of domain semantics.
Solution Approach 2:
The system performs preliminary data preparation by automatically generating sentence pairs and computing their similarity scores before the actual embedding training begins. This pre-computation creates a ready-to-use training dataset that maintains high quality without manual intervention, saving significant time in the overall workflow.
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for perform predictive data analysis operations using natural language input data. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by using sentence embedding machine learning models that are trained in coordination with similarity-based machine learning models.