Open Information Extraction Using Nested Verb Relationships
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Solution Overview
Problem
Current open information extraction systems struggle to accurately represent complex sentences and extract structured information from text, particularly in non-English languages, due to limitations in training data and reliance on neural networks, which are not explainable or adaptable enough to handle variations in sentence structure.
Innovation Solution
The proposed solution involves a method and apparatus that utilize a combination of manual coding and AI machine learning to identify and resolve relationships between verbs, including nested relationships, using syntax dependency trees, parts of speech tags, and tokens, allowing for the extraction of richer information and expansion of tuples, and operates on a universal dependencies framework applicable to over 100 languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If neural networks are used for open information extraction, then extraction speed is improved, but explainability and adaptability to sentence structure variations deteriorate
Solution Approach 1:
The patent segments the information extraction process into multiple stages: syntax dependency tree construction, verb relationship resolution, and nested relationship identification. This segmentation allows each stage to handle specific aspects of the extraction task independently, improving both speed and adaptability by avoiding the need for a single complex neural network to handle all variations.
Solution Approach 2:
The patent implements nested relationships where verb relationships are identified within the context of syntax dependency trees, and tuples are expanded based on nested verb interactions. This nesting approach enables the system to handle complex sentence structures by organizing extraction operations in hierarchical levels, maintaining both efficiency and adaptability.
2Adaptability or versatility
If manual coding is used for information extraction rules, then adaptability to different languages is improved, but extraction speed and automation deteriorate
Solution Approach 1:
The patent employs a universal dependencies framework that provides a common syntactic representation for multiple languages. This framework serves as a multi-functional foundation that can be applied across over 100 languages without requiring language-specific extraction systems, thereby maintaining high adaptability while enabling efficient automated processing through shared rules and structures.
3Measurement precision
If complex sentence structures are processed, then information extraction precision is improved, but processing time and system complexity deteriorate
Solution Approach 1:
The patent performs preliminary syntax dependency tree construction and verb relationship resolution before final tuple extraction. By preparing the syntactic structure and identifying verb relationships in advance, the system reduces the complexity of processing complex sentences during the main extraction phase, thereby improving precision without proportionally increasing processing time.
Data Source
AI summary
Systems, methods, apparatuses, and computer program products for natural language processing are provided. One method may include utilizing a trained machine learning model to learn syntax dependency patterns and parts of speech tag patterns of text based on labeled training data. The method may also include contextualizing vector embeddings from a language model for each word in the text, and extracting relationships for a given fragment of the text based on the contextualization. The method may further include resolving relationships between identified verbs based on a plurality of heuristics to identify the syntax dependency patterns, identifying nested relationships, and capturing metadata associated with the nested relationships. The method also includes performing, by utilizing the extracted relationships, at least one of open-domain question answering and natural language question answering, answering questions posed by a user, summarizing documents, or measuring information overlap and information disagreement.