Automated Lexico-Syntactic Pattern Extraction for Named Entity Relations
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Solution Overview
Problem
Current methods for identifying semantic relationships between named entities in text are inefficient due to the difficulty in establishing reliable lexico-syntactic patterns, leading to a large number of non-responsive search results.
Innovation Solution
A computer-implemented system and method that automatically generates lexico-syntactic patterns by retrieving text strings with named entities, extracting syntactic patterns, and generating generalized rules to identify candidate instances of semantic relations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual establishment of lexico-syntactic patterns is used, then pattern reliability can be improved, but time consumption and labor requirements increase significantly
Solution Approach 1:
The system performs self-service by automatically extracting and generating lexico-syntactic patterns from text data without requiring manual establishment. The pattern extraction module automatically analyzes text strings and generates patterns that reflect semantic relations between named entities, eliminating the need for manual pattern creation while maintaining reliability through systematic automated analysis
Solution Approach 2:
The patent replaces the mechanical manual process of pattern establishment with an automated computational system. The pattern extraction module uses computational algorithms to automatically identify and generate lexico-syntactic patterns from text, substituting human manual analysis with automated mechanical processing that achieves both speed and reliability
2Loss of time
If automated pattern generation is used, then time consumption is reduced, but the complexity of the system increases
Solution Approach 1:
The system segments the complex task of semantic relation extraction into distinct functional modules: a text retrieval module that obtains text strings, a pattern extraction module that identifies lexico-syntactic patterns, and a rule generation module that creates generalized rules. This segmentation divides the complex system into manageable, independent components that work together to achieve automated pattern generation
Solution Approach 2:
The pattern extraction module serves multiple functions: it extracts patterns from text strings, identifies semantic relations between named entities, and generates generalized rules. This multi-functionality reduces the need for separate specialized components, thereby managing system complexity while achieving comprehensive automated processing
3Loss of information
If search for sentences with named entities is performed, then relevant information can be retrieved, but the number of non-responsive results increases
Solution Approach 1:
The system dynamically adapts the search process by first extracting actual lexico-syntactic patterns from the text data and then using these extracted patterns to filter and refine search results. This dynamic approach allows the system to adjust the search criteria based on the actual content and semantic relations found in the text, improving result relevance
Solution Approach 2:
The system implements feedback by extracting patterns from retrieved text strings and using these extracted patterns to refine subsequent search and filtering operations. The pattern extraction module analyzes the retrieved text and generates feedback information in the form of generalized rules that guide future retrieval operations, creating an iterative improvement cycle that reduces non-responsive results
Data Source
AI summary
A system and method of developing rules for text processing enable retrieval of instances of named entities in a predetermined semantic relation (such as the DATE and PLACE of an EVENT) by extracting patterns from text strings in which attested examples of named entities satisfying the semantic relation occur. The patterns are generalized to form rules which can be added to the existing rules of a syntactic parser and subsequently applied to text to find candidate instances of other named entities in the predetermined semantic relation.