Semantic Text Fragment Alignment in Unstructured Documents
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Solution Overview
Problem
Current systems struggle to organize and align fragmented text in documents with no fixed layout or sufficient tabular alignment indicators, such as scanned PDFs, into a readily understandable format, as they rely on spatial and formatting features rather than semantic meaning.
Innovation Solution
A method that identifies and clusters text fragments using a relation model based on semantic relatedness, generating composite text objects and optimizing their alignment within a table, leveraging word embeddings and cosine similarity to improve table alignment and simplify the format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current systems use spatial and formatting features to organize text fragments, then the organization process is simple, but the alignment accuracy deteriorates in documents with no fixed layout or sufficient tabular alignment indicators
Solution Approach 1:
The patent replaces spatial and formatting-based organization (mechanical system) with semantic meaning-based organization using natural language processing. The system uses semantic analysis to understand the meaning and relationships between text fragments, enabling accurate alignment in documents without fixed layouts by substituting structural cues with semantic understanding.
Solution Approach 2:
The patent changes the organizational parameter from spatial position and formatting attributes to semantic meaning and contextual relationships. By transforming the basis of organization from physical document structure to semantic content, the system achieves improved alignment accuracy while adapting to various document formats including those without fixed layouts.
2Reliability
If systems rely on spatial and formatting features for text organization, then the processing speed is fast, but the reliability deteriorates when documents lack sufficient alignment indicators
Solution Approach 1:
The system replaces reliance on spatial and formatting features with semantic analysis, improving reliability for documents lacking fixed layouts. By using natural language processing to understand textual relationships rather than positional cues, the system achieves more reliable organization across diverse document formats.
Solution Approach 2:
The system performs preliminary semantic analysis and relationship extraction to establish reliable text fragment organization. By pre-processing the semantic content and identifying relationships before final alignment, the system improves reliability while managing processing time through structured analysis.
3Measurement precision
If systems use semantic meaning to organize text fragments, then the alignment accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent replaces simple spatial organization with semantic analysis using natural language processing. This substitution enables accurate alignment by understanding textual meaning and relationships, accepting increased computational complexity as necessary for achieving reliable results in unstructured documents.
Solution Approach 2:
The system introduces semantic analysis as an intermediary layer between raw text fragments and final alignment. This intermediary process extracts meaningful relationships and contextual information, enabling accurate organization while managing computational complexity through structured semantic processing rather than brute-force methods.
Data Source
AI summary
Organizing and/or aligning fragments of text that are included in a set of physical and/or digital documents so that the arrangement of the text fragments is in a readily understandable and meaningful format for a given reader. This organization and/or alignment uses a relation model of the various text fragments to correlate a meaning between and amongst the various text fragments to ultimately determine the final alignment and/or arrangement of those text fragments.