Natural Language Processing Clause Segmentation for Information Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing natural language processing (NLP) methods are cumbersome, time-consuming, and require significant processing power and storage space, making it inefficient to convert complex natural language sentences into computer-readable formats for information extraction.
Innovation Solution
The method involves identifying verbal blocks in natural language sentences, splitting them into logical clauses, disambiguating ambiguous blocks, and constructing computer-readable primitive sentences by duplicating shared noun phrases, allowing for efficient information extraction using simple regular expression-like patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing NLP methods use complete grammar and semantic trees to process natural language, then information extraction accuracy is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the complex natural language processing task into distinct phases: sentence splitting into clauses, clause type identification, and selective information extraction. This segmentation allows the system to process only relevant portions of sentences using appropriate methods, avoiding the need to apply complete grammar analysis to every sentence, thus reducing processing time while maintaining accuracy for extracted information
Solution Approach 2:
The patent applies different processing qualities to different parts of the input text. Simple sentences are processed with basic clause splitting, while ambiguous sentences receive additional clause type analysis. This local quality approach ensures that computational resources are concentrated on sentences requiring deeper analysis, improving overall processing efficiency without compromising extraction accuracy
2Adaptability or versatility
If existing NLP methods construct complete semantic trees with full grammar, then language understanding capability is improved, but storage requirements and system complexity increase
Solution Approach 1:
The patent divides the language understanding task into modular components: sentence splitting, clause identification, clause type determination, and information extraction. Each module handles a specific aspect of processing, reducing overall system complexity while maintaining comprehensive language understanding capability through the coordinated operation of these simplified modules
Solution Approach 2:
The patent extracts only the essential elements needed for information extraction from complete semantic trees. By identifying clause types and extracting relevant information directly from clauses, the system obtains the necessary language understanding capability without constructing and maintaining the full complexity of complete semantic trees and grammar rules
3Reliability
If existing NLP methods process every sentence with complete grammar analysis, then processing thoroughness is improved, but processing speed decreases
Solution Approach 1:
The patent applies partial action by processing sentences to the extent necessary for information extraction. Not every sentence requires complete grammar analysis - the system performs clause splitting on all sentences and applies additional clause type identification only when needed, achieving sufficient processing thoroughness for extraction purposes while maintaining high processing speed
Solution Approach 2:
The patent applies different processing thoroughness to different sentences based on their characteristics. Sentences with clear structures receive basic processing, while ambiguous sentences receive more thorough clause type analysis. This local quality approach ensures processing reliability for critical cases while maintaining overall processing speed across large volumes of text
Data Source
AI summary
Systems and methods for converting a natural language sentence into a computer-readable primitive sentence and extracting information therefrom are disclosed. A method includes identifying, by a processing device, a verbal block in the natural language sentence, splitting, by the processing device, the natural language sentence into one or more logical clauses, determining, by the processing device, a type for each logical clause, where the type indicates whether each logical clause contains an ambiguous verbal block, disambiguating, by the processing device, the ambiguous verbal block within each logical clause, where each verbal block is considered independently of a noun phrase, and constructing, by the processing device, the computer-readable primitive sentence for each ambiguous verbal block by duplicating a shared noun phrase of the ambiguous verbal block. The computer-readable primitive sentence improves functioning of a computing device by allowing the computing device to process the natural language sentence to obtain information therefrom.