Delimiter-Tolerant Parsing With Variable Tokens for Ambiguous Text
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Solution Overview
Problem
Existing automated text parsing systems struggle with ambiguity and complexity in sentence parsing, leading to misinterpretations and user fatigue due to reliance on text-based representations, lacking intuitive user interfaces for accurate review and correction.
Innovation Solution
An adaptive parsing system utilizing delimiter tolerance and variable tokens to dynamically adjust parsing strategies, incorporating machine-readable markup and auto-correction capabilities without requiring a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated text parsing systems use algorithmic parsing without human intervention, then parsing speed and productivity are improved, but parsing accuracy deteriorates due to inherent language ambiguities
Solution Approach 1:
The system enables users to directly annotate and correct parsed text without requiring complex graphical interfaces. Users can mark errors in the parsed output, and the system automatically learns from these corrections to improve future parsing attempts, making the system self-improving through user feedback
Solution Approach 2:
The system incorporates feedback mechanisms where users review and correct parsed text, and these corrections are fed back into the parsing algorithm to refine its performance. This continuous feedback loop enables the system to learn from user corrections and improve accuracy over time
2Device complexity
If systems use text-based representations for parsing output, then device complexity is reduced, but ease of operation deteriorates due to user fatigue and difficulty in reviewing corrections
Solution Approach 1:
The system creates a simplified textual representation of the parsing structure that users can easily review and annotate. Instead of requiring complex graphical tree structures, the system uses formatted text outputs that preserve parsing information while being much easier for users to read and correct
Solution Approach 2:
The system introduces an intermediary layer between the complex parsing algorithm and the user interface. This intermediary translates complex hierarchical parse trees into more manageable textual representations that users can easily review and correct without needing to understand the underlying parsing complexity
3Manufacturing precision
If systems rely on fixed delimiters for parsing, then manufacturing precision is improved, but adaptability deteriorates when encountering variable token formats
Solution Approach 1:
The parsing system dynamically adjusts its delimiter detection based on the input text. Instead of relying on fixed delimiters, the system learns from the text itself to identify appropriate delimiters and token boundaries, allowing it to adapt to various formats while maintaining consistent parsing results
Solution Approach 2:
The system changes its parsing parameters adaptively based on the input data. It detects patterns in the text and adjusts delimiter sensitivity, tokenization rules, and parsing parameters in real-time to handle variable formats while maintaining high parsing accuracy
Data Source
AI summary
This invention presents an adaptive parsing system with a processor designed to enhance text data structuring. It initiates parsing with a default delimiter, adjusting to extra delimiters by employing variable tokens, thereby optimizing the parsing strategy for diverse data formats. The system trims whitespace, categorizes tokens, and resolves ambiguities using heuristic rules and a machine learning model trained on previously parsed tokens. A user interface allows for manual correction, feeding back into the model for continuous improvement. The parsed data is output in structured formats (JSON, XML, CSV), suitable for various database systems. Integrated into a larger data processing framework, it provides real-time feedback and supports scalability and collaboration in cloud environments. This adaptive approach to parsing addresses data format diversity, ensuring accuracy and efficiency in data interpretation.