Semantic Abstraction for Information Extraction via Dependency Parse Trees
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Solution Overview
Problem
Information extraction from natural-language text is challenging due to the numerous linguistic variants that express the same semantics, making it time-consuming to develop effective information extraction programs that handle these variations.
Innovation Solution
The method involves translating natural-language dependency parse trees into a collection of actions, roles, and contextual predicates using a hardware processor, where actions include verbs with attributes, roles include arguments for the verbs, and contextual predicates include modifiers, facilitating information extraction by creating a mapping from nodes of the dependency parse trees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If expressive information extraction systems are developed to handle linguistic variants, then the ability to extract information from diverse text forms is improved, but the development time and complexity increase significantly
Solution Approach 1:
The patent introduces dependency parse trees as an intermediary representation layer between raw natural language text and information extraction logic. The system first parses text into dependency trees, then applies extraction rules to the structured tree format. This intermediary structure standardizes diverse linguistic variants into a common format, enabling efficient information extraction without requiring separate handling for each linguistic variation.
Solution Approach 2:
The system transforms the parameter representation of text from unstructured raw strings to structured dependency parse trees with defined node types, edge relationships, and hierarchical levels. By changing the representation parameters from free-form text to constrained tree structures, the system enables systematic processing of linguistic variants through rule-based extraction on the standardized tree format.
2Adaptability or versatility
If expressive information extraction systems are developed to handle linguistic variants, then the ability to extract information from diverse text forms is improved, but the program complexity increases significantly
Solution Approach 1:
The patent segments the information extraction process into distinct modular components: (1) text parsing into dependency trees, (2) tree traversal and node identification, (3) rule-based extraction application, and (4) result compilation. Each component handles a specific aspect of processing, reducing overall program complexity by breaking down the complex task of handling linguistic variants into manageable, independent modules that can be developed and maintained separately.
Solution Approach 2:
The dependency parse tree structure serves as a universal representation that can handle multiple types of linguistic variants (different sentence structures, grammatical forms, expressions) through a single standardized format. The extraction rules operate universally on this structure regardless of the specific linguistic variant, eliminating the need for separate processing logic for each variant type and reducing program complexity.
Data Source
AI summary
A method includes receiving one or more natural language dependency parse trees as input. A hardware processor is used for processing the dependency parse trees by creating a mapping from nodes of the one or more dependency parse trees into actions, roles and contextual predicates. The mapping is used for information extraction. The actions include the verbs along with attributes of the verbs. The roles include arguments for the verbs. The contextual predicates include modifiers for the verbs.