NLP Promise Identifiers for Document Reference Resolution
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Solution Overview
Problem
Conventional natural language processing (NLP) systems fail to automatically identify and resolve references such as footnotes or appendices within documents, requiring manual intervention from users to retrieve relevant information, which increases processing time and inefficiency.
Innovation Solution
A computer-implemented method that uses an NLP engine to process documents, generate promise identifiers for references, and resolve these identifiers to provide associated data, enabling automatic matching and retrieval of supplemental information without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention is used to retrieve reference information, then information retrieval accuracy is maintained, but processing time increases and efficiency decreases
Solution Approach 1:
The NLP system automatically identifies references and retrieves associated information without requiring manual user intervention. The system serves itself by autonomously processing document references, extracting promise identifiers, and resolving them to obtain supplemental information, thereby eliminating the trade-off between manual accuracy and automated speed.
2Productivity
If automated NLP processing is implemented, then processing efficiency is improved, but the system's ability to accurately identify and resolve references is insufficient
Solution Approach 1:
The system introduces promise identifiers as intermediary elements that bridge the main document text and supplemental reference information. These unique identifiers act as mediators that enable automated NLP systems to accurately match references with their corresponding content, thereby maintaining high reference resolution accuracy while achieving automated processing efficiency.
Solution Approach 2:
The document structure is segmented into distinct components with unique promise identifiers assigned to each reference and its associated supplemental information. This segmentation allows the NLP system to independently identify and resolve each reference accurately, improving overall reference resolution reliability while maintaining automated processing efficiency.
3Measurement precision
If manual reference retrieval is performed, then information accuracy is maintained, but user effort and operational complexity increase
Solution Approach 1:
The system performs self-service by automatically identifying references, generating promise identifiers, and retrieving associated supplemental information without requiring user effort. This eliminates the need for manual reference retrieval while maintaining information accuracy through automated NLP processing and precise identifier matching.
4Productivity
If automated reference identification is implemented, then operational efficiency is improved, but device complexity increases
Solution Approach 1:
The promise identifier acts as a simple intermediary mechanism that reduces system complexity. Instead of implementing complex automated reference resolution logic, the system uses these unique identifiers as straightforward mediators that enable efficient automated processing while keeping the overall system architecture simple and manageable.
Data Source
AI summary
Aspects of the invention include a computer-implemented method for generating promise identifiers for documents. Aspects include processing a document including a reference, wherein processing includes performing natural language processing (NLP) the document, and identifying the reference included in the document. Aspects also include generating a promise identifier for the reference in the document, and responsive to processing the document, resolving the promise identifier for the reference by providing data of the reference associated with the promise identifier. Aspects of the invention also include a computer program product and system for generating promise identifiers for documents.