Entity Scoring Model Using Precomputed Hyperparameters
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Solution Overview
Problem
Existing natural language processing systems face challenges in performing real-time semantically aware tasks due to complex non-linear operations, which hinder efficient document scoring and modification recommendations, limiting their ability to process documents in a time-efficient manner.
Innovation Solution
The implementation of entity scoring machine learning models that use precomputed hyperparameters for generating predicted entity scores through linear combinations, reducing computational complexity and enabling real-time or near-real-time natural language processing by delegating processing tasks to training time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex non-linear operations are used in natural language processing systems, then semantic intelligence and accuracy are improved, but processing speed and runtime efficiency deteriorate
Solution Approach 1:
The patent precomputes hyperparameters during training time and stores them for runtime use. Specifically, entity prevalence scores, entity quality scores, and section-level hyperparameters are calculated in advance and cached, eliminating the need for complex non-linear computations during real-time document scoring. This preliminary action resolves the contradiction by shifting computational burden from runtime to training time.
Solution Approach 2:
The patent transforms complex non-linear operations into simple linear combinations by changing the computational parameters. Instead of performing complex semantic analysis at runtime, the system uses precomputed hyperparameters (entity prevalence, entity quality, section importance) in linear combinations to generate document scores. This parameter transformation maintains semantic intelligence while dramatically improving processing speed.
2Measurement precision
If complex non-linear operations are performed in real-time, then accurate document scoring is achieved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary computation of all complex semantic relationships during training, storing results as hyperparameters. During real-time document scoring, only simple linear combinations of these precomputed values are needed, reducing processing time from minutes to seconds while maintaining scoring accuracy.
Solution Approach 2:
The patent creates simplified copies of complex semantic relationships in the form of precomputed hyperparameters. Instead of recreating complex non-linear operations during runtime, the system uses cached copies of entity prevalence scores, entity quality scores, and section-level metrics that capture the essential semantic information in a computationally efficient format.
3Reliability
If entity scoring models process all entities in documents, then comprehensive analysis is achieved, but computational complexity and processing overhead increase
Solution Approach 1:
The patent changes the computational approach from processing all entities with full semantic analysis to using precomputed entity hyperparameters in linear combinations. Entity prevalence scores and entity quality scores are calculated once during training and reused across multiple documents, reducing computational complexity while maintaining comprehensive analysis through the aggregated entity-level metrics.
Data Source
AI summary
There is a need for more accurate and more efficient natural language solutions with greater semantic intelligence. This need can be addressed, for example, by natural language processing techniques that utilize predictive entity scoring. In one example, a method includes determining an overall prevalence score for the input entity data object with respect to a scored document corpus and a target section; determining a qualified prevalence score for the input entity data object with respect to a high-scoring subset of the scored document corpus; processing the input entity data object using an entity scoring machine learning model to generate the predicted entity score, wherein the entity scoring machine learning model may characterized by a plurality of multiplicative hyper-parameters and one or more additive hyper-parameters; and performing one or more prediction-based actions based at least in part on the predicted entity score.