Multi-Score Answer Mining for High Recall Retrieval

The patent changes the evaluation parameter from a single binary filter (pass/fail) to a multi-dimensional scoring system. Each candidate text receives multiple scores from different models (semantic matching score, keyword matching score, position score, etc.), and the final selection is based on comprehensive scoring rather than simple threshold filtering. This resolves the contradiction by maintaining high recall while improving efficiency through structured evaluation.

The patent combines multiple evaluation models and scoring mechanisms into a composite answer selection system. Different models (semantic matching, keyword matching, position-based) are combined to form a comprehensive evaluation framework. This composite approach ensures that correct answers are not filtered out by any single model's limitations while maintaining efficient selection through the integrated scoring system.

The patent segments the answer selection task into multiple independent scoring components, each handled by a separate model (semantic matching model, keyword matching model, position scoring model). Each model focuses on a specific aspect of answer quality, and their results are aggregated. This segmentation improves measurement precision by specializing each model while managing complexity through modular design.

The patent creates a universal scoring framework that can accommodate multiple different evaluation models. The framework provides a standardized interface for integrating various scoring mechanisms (semantic, keyword, position, etc.) and aggregating their results. This multi-functional design improves correctness by allowing diverse evaluation approaches while controlling complexity through a unified structure.