NLP Dependency Parsing for Structured Semantic Extraction
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Solution Overview
Problem
Natural language processing systems face challenges in accurately extracting and representing complex relationships within unstructured text, leading to inefficiencies in information extraction and semantic analysis.
Innovation Solution
A natural language processing system that applies semantic labels to words or phrases based on part-of-speech types, generates tokens representing these labels, and provides a structured representation to downstream systems, enabling more accurate semantic analysis by maintaining data on interactions between elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language processing systems use basic text analysis methods, then the system complexity is low, but the accuracy of extracting complex relationships is insufficient
Solution Approach 1:
The patent segments unstructured text into discrete linguistic units (tokens, phrases, clauses) and assigns structured labels to each segment. This segmentation allows the system to manage complexity by breaking down complex relationship extraction into manageable labeled components, thereby improving accuracy without overwhelming system complexity.
Solution Approach 2:
The patent transforms unstructured text data into structured data by applying multiple labeling parameters (part-of-speech tags, semantic roles, dependency relationships). This parameter transformation enables the system to capture complex relationships systematically, improving extraction accuracy while maintaining organized data structures that don't excessively increase system complexity.
2Measurement precision
If the system generates detailed structured representations with multiple labels and tokens, then the accuracy of machine readable data improves, but the processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary linguistic analysis steps (tokenization, part-of-speech tagging, dependency parsing) before main semantic extraction. These preliminary actions prepare the data in advance with structured labels, making the subsequent relationship extraction more efficient and accurate without requiring excessive computational resources during the main processing phase.
Solution Approach 2:
The patent introduces intermediate structured representations (labeled tokens, dependency trees, semantic roles) as mediators between raw unstructured text and final extracted relationships. These intermediaries organize information in a way that accelerates processing by providing a structured framework, reducing the computational burden of direct relationship extraction from unstructured text.
3Adaptability or versatility
If traditional algorithms process unstructured data directly, then the method is simple, but the ability to analyze detailed information is limited
Solution Approach 1:
The patent creates structured copies of unstructured text data with added linguistic annotations and labels. These copied representations maintain the original information while adding structured metadata, enabling traditional algorithms to process detailed information effectively without requiring fundamentally new processing methods, thus balancing adaptability with manageable complexity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for information extraction using natural language processing. One of the methods includes determining, for one or more tokens from a plurality of tokens that represent an unstructured sentence, a token type from a plurality of predetermined token types that indicates an element type for a phrase that corresponds to the token and has one or more properties using dependency data and a part-of-speech label for the token; assigning, for a token whose associated dependency data indicates that the token has a child, data for the child token to one of the one or more properties for the token type of the token; and providing, for use by a downstream semantic system and for the token, a textual representation of the phrase for the token and the phrases for one or more of the child tokens.