Natural Language Text Analysis via Segmentation and Categorization
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Solution Overview
Problem
Current text analysis methods, particularly in natural language processing, face challenges in efficiently structuring and analyzing unconstrained natural language texts, leading to difficulties in querying, identifying inconsistencies, and executing user commands or engaging in dialogues without relying on pre-programmed vocabularies or complex statistical analyses.
Innovation Solution
A non-statistical text analysis system that segments and categorizes unconstrained natural language texts into individual terms, groups them into expressions, and organizes them into information blocks, which can be standardized and stored in databases for querying, inconsistency detection, and command execution, using conventional parsing and category lexicons to assign semantic categories and manage ambiguity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If statistical text analysis methods are used to analyze unconstrained natural language texts, then the system can handle diverse text inputs, but the system becomes unreliable and unpredictable in its conclusions
Solution Approach 1:
The patent segments natural language text into discrete linguistic units (words, phrases, sentences) and applies structured grammatical analysis to each unit. This segmentation allows the system to process diverse text inputs through consistent, rule-based linguistic operations, achieving both versatility and reliability.
Solution Approach 2:
The patent transforms unstructured text data into structured linguistic parameters (part of speech, syntactic role, semantic category) through systematic parsing rules. This parameter transformation enables reliable comparison and analysis across different text inputs while maintaining adaptability to various input formats.
2Adaptability or versatility
If AI or statistical analysis methods are used to create analysis rules dynamically, then the system can adapt to new contexts, but the system loses predictability and cannot substantiate its assertions
Solution Approach 1:
The patent establishes comprehensive linguistic rules, grammatical frameworks, and parsing algorithms before text analysis begins. These pre-defined linguistic principles provide a stable foundation for analyzing diverse texts, ensuring both adaptability to new contexts and traceability of analytical conclusions through documented linguistic reasoning.
3Device complexity
If conventional text analysis approaches are used without structured information blocks, then the analysis process is simpler, but information retrieval and querying become inefficient
Solution Approach 1:
The patent segments analyzed text into discrete information blocks organized by linguistic and semantic categories. This segmentation structures information for efficient retrieval and querying while maintaining a relatively simple analysis process based on established linguistic principles rather than complex computational methods.
Data Source
AI summary
Systems and methods for performing non-statistical text analysis are provided. Essentially unconstrained natural language text may be segmented into individual terms, which are assigned a semantic category with the aid of a category lexicon and category association table. Categorized terms may be grouped into expressions if they commonly refer to a concept. Terms, as well as information about their categories and groupings into expressions, may be organized into information blocks, which each may contain a discrete piece of information about a text. Information blocks may be used to populate a database about the text. Information blocks may be queried to answer a question, be compared to identify inconsistencies in the text, be used to identify functions to execute from a user's command, or how to respond to a user during a dialog.