Ontological Relation Extraction Using Shallow Lexico-Syntactic Patterns
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Solution Overview
Problem
Existing natural language processing systems struggle to accurately identify both hypernym and part-whole relations from manual text data, as they rely on complete lexico-syntactic patterns and are not effective in extracting relations from documents with specific content types, such as manuals, and do not utilize partial or generalized patterns.
Innovation Solution
The method employs shallow and generalized lexico-syntactic patterns to extract term features, identify coordinate relations, and infer hypernym and part-whole relations, using Conditional Maximum Entropy modeling to learn ontological relations from manual data, enabling the identification of domain-specific relations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complete lexico-syntactic patterns are used to extract ontological relations, then extraction precision is improved, but the system cannot handle partial or generalized patterns and fails to extract relations from manual text data
Solution Approach 1:
The patent segments complete lexico-syntactic patterns into partial patterns by identifying and extracting key relational components. Instead of requiring full pattern matches, the system breaks down patterns into essential elements that can be independently identified and combined, enabling both precise extraction and flexibility in handling various text structures.
Solution Approach 2:
The system applies partial pattern matching rather than requiring complete pattern matches. By using partial lexico-syntactic patterns, the system can identify ontological relations even when the full pattern is not present, thus maintaining precision while gaining the ability to handle generalized and varied text structures.
2Ease of operation
If open-domain syntactic analysis techniques are applied, then general language processing is improved, but the system fails to extract part-whole relations from manual text data with ambiguous contexts
Solution Approach 1:
The patent applies local quality by adapting the analysis approach to the specific characteristics of manual text data. Instead of using a uniform open-domain approach, the system employs specialized lexico-syntactic patterns tailored for manual texts, enhancing both ease of operation and precision for this specific domain.
Solution Approach 2:
The system changes key parameters of the syntactic analysis by using domain-specific lexico-syntactic patterns instead of general-purpose ones. This parameter change enables the system to handle ambiguous contexts in manual texts more effectively, improving relation extraction accuracy while maintaining operational ease.
3Measurement precision
If hypernym relations are used as semantic constraints, then part-whole relation extraction is improved for specific patterns, but the system cannot handle long-distance dependencies and generalized patterns
Solution Approach 1:
The patent creates a universal lexico-syntactic pattern framework that can handle multiple types of ontological relations (hypernym, part-whole, coordinate) and various pattern lengths. This multi-functional approach eliminates the need for separate handling of different relation types and pattern complexities, reducing system limitations while maintaining high precision.
Solution Approach 2:
The system adds a new dimension to relation extraction by incorporating lexico-syntactic patterns that capture long-distance dependencies. This dimensional extension allows the system to go beyond immediate syntactic relationships and identify ontological connections across longer text spans, overcoming previous system limitations.
4Adaptability or versatility
If traditional NLP tools and resources are used, then general processing is maintained, but the system cannot accurately identify domain-specific ontological relations from manual text data
Solution Approach 1:
The patent applies preliminary action by pre-processing manual text data to identify domain-specific terminology and relationships before general NLP processing. This preliminary step prepares the data with domain-specific annotations and structures, enabling traditional NLP tools to accurately identify domain-specific ontological relations that would otherwise be missed.
Data Source
AI summary
Embodiments of an ontological determination method for use in natural language processing applications are described. In one embodiment, shallow lexico-syntactic patterns are applied to identify relations by extracting term features to distinguish relation terms from non-relation terms, identifying coordinate relations for every adjacent terms; identifying short-distance ontological (e.g., hypernym or part-whole relations) for other adjacent terms based on term features and lexico-syntactic patterns; and then inferring long-distance hypernym and part-whole relations based on the identified coordinate relations and the short-distance relations.