Linguistic Pattern Extraction Rules Abstraction Control
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Solution Overview
Problem
Existing natural language processing systems face challenges in generating linguistic pattern extraction rules that accurately extract relevant patterns from documents, often resulting in too many inappropriate matches due to insufficient abstraction, which fails to meet user analysis needs.
Innovation Solution
A computer-implemented method that generates linguistic pattern extraction rules by abstracting user-selected text examples stepwise based on linguistic context using a set of abstraction rules, ordering rules by abstraction level, and applying machine learning to refine patterns, ensuring relevant pattern extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If linguistic pattern extraction rules are generated with low abstraction level, then more specific patterns are extracted, but too many inappropriate matches occur
Solution Approach 1:
The system dynamically adjusts the abstraction level of linguistic pattern extraction rules based on user feedback and analysis context. The abstraction level is not fixed but can be modified during the extraction process to optimize the balance between precision and quantity of extracted patterns, resolving the contradiction by making the system adaptable to specific analysis needs.
Solution Approach 2:
The system changes the abstraction level parameter of linguistic pattern extraction rules to control the trade-off between extraction precision and quantity. By adjusting this parameter, the system can generate rules that are neither too specific (causing excessive inappropriate matches) nor too abstract (losing precision), thus resolving the technical contradiction.
2Measurement precision
If multiple levels of abstraction rules are applied, then pattern extraction accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the linguistic pattern extraction process into multiple abstraction levels, where each level handles specific aspects of pattern matching. This segmentation allows the complex task of accurate pattern extraction to be divided into manageable components, reducing overall system complexity while maintaining high accuracy through coordinated operation of multiple specialized rules.
Solution Approach 2:
The system dynamically selects and applies only the necessary abstraction levels based on the specific extraction task and user needs, rather than always applying all levels. This dynamic approach reduces system complexity by activating only the required components while maintaining the capability to achieve high accuracy when needed.
3Measurement precision
If user feedback is incorporated to refine rules, then extraction relevance improves, but processing time increases
Solution Approach 1:
The system incorporates user feedback into the linguistic pattern extraction rule refinement process, where user corrections and preferences are used to improve the relevance of extracted patterns. This feedback mechanism allows the system to learn from user interactions and continuously improve extraction quality, resolving the contradiction by making the system adaptive to user needs while managing processing time through efficient feedback integration.
Solution Approach 2:
The system performs preliminary refinement of linguistic pattern extraction rules using available data and patterns before full extraction, incorporating anticipated user preferences in advance. This preliminary action reduces the need for extensive iterative refinement during actual extraction, thereby improving extraction relevance while minimizing additional processing time.
Data Source
AI summary
Generating rules to automatically extract linguistic patterns from documents is provided. A first plurality of linguistic pattern extraction rules corresponding to a user-selected text example from a document is generated according to a first abstraction rule of a plurality of abstraction rules. Each respective linguistic pattern extraction rule of the first plurality of linguistic pattern extraction rules having a first identified level of abstraction. The first plurality of linguistic pattern extraction rules ordered by the first identified level of abstraction is presented in a first list to a user via a user interface. A selection of one particular linguistic pattern extraction rule is received from the first list by the user via the user interface. That one particular linguistic pattern extraction rule selected by the user is applied to the document to automatically extract user-desired linguistic patterns similar to the user-selected text example from the document.