NLP Sentence Matching via Probabilistic Relevance Scoring
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Solution Overview
Problem
Existing natural language processing (NLP) systems for automatic sentence condition matching in documents often rely on exact matches or user interaction, failing to accurately identify standard conditions or clauses without human supervision, and struggle with sentences that have similar meanings despite differing constructions.
Innovation Solution
A system utilizing a probabilistic relevance weighting model and NLP rules with a linguistic dictionary to compute normalized relevance scores and identify semantic matches, enabling automatic assertion of standard terms, clauses, or conditions in documents without requiring exact matches or human supervision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exact match is used to retrieve target sentences, then retrieval precision is improved, but retrieval completeness deteriorates because highly similar sentences with different wording are missed
Solution Approach 1:
The patent transforms the matching parameter from exact string equality to semantic similarity by computing normalized relevance scores based on probabilistic relevance weighting models. This allows sentences with different wording to be matched if they convey the same meaning, resolving the contradiction between precision and completeness.
Solution Approach 2:
The patent introduces an intermediary semantic analysis layer that computes relevance scores between query sentences and document sentences. This intermediary mechanism translates semantic equivalence into a quantitative score, enabling the system to retrieve both exact matches and semantically equivalent sentences without sacrificing precision.
2Productivity
If NLP-based semantic matching is used, then retrieval completeness is improved, but system complexity increases due to the need for probabilistic models and linguistic dictionaries
Solution Approach 1:
The patent segments the complex NLP task into distinct modules: a probabilistic relevance weighting model for initial scoring, a normalized relevance score computation step, and a threshold-based filtering mechanism. This segmentation reduces system complexity by breaking down the overall matching process into manageable, independent components.
Solution Approach 2:
The system uses self-contained linguistic resources (probabilistic relevance weighting models and normalized scoring mechanisms) that operate autonomously without requiring external human intervention or complex configuration, thereby reducing operational complexity while maintaining high retrieval completeness.
3Measurement precision
If user interaction and configuration are required to assert standard conditions, then assertion accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The patent implements self-service assertion by automatically computing normalized relevance scores and comparing them against thresholds to determine the presence of standard conditions. The system autonomously performs assertion without requiring user configuration or interaction, thereby maintaining high accuracy while dramatically improving ease of operation.
Solution Approach 2:
The patent performs preliminary action by pre-computing and storing probabilistic relevance weighting models and normalized relevance scores for standard conditions. This preliminary preparation enables the system to quickly and accurately assert standard conditions in real-time applications without requiring user intervention during the assertion process.
Data Source
AI summary
One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to automatic sentence condition matching using natural language processing (NLP). The computer-implemented system can comprise a memory that can store computer-executable components and a processor that can execute the computer-executable components, wherein the computer-executable components can comprise an extraction module that can use a probabilistic relevance weighting model to retrieve a first sentence from a document by computing a normalized relevance score of the first sentence based on a relevance weighting score of a second sentence from a dictionary of query sentences. The computer-executable components can further comprise a resolution module that can use a set of NLP rules and a linguistic dictionary to automatically identify whether the first sentence and the second sentence have a same meaning based on the normalized relevance score being above a defined threshold.