NLP Information Extraction from Multi-Format Documents
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Solution Overview
Problem
Existing technologies face challenges in extracting information from electronic documents with varying formats, as machine learning algorithms are typically trained for specific formats, making it difficult to analyze documents with different structures.
Innovation Solution
The use of a natural language processing (NLP) algorithm, implemented through neural networks, to identify usable information from documents in various formats. This involves supervised training with examples of specific document types, allowing the NLP algorithm to distinguish between label and data points-of-interest and match them using spatial and coherence analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning algorithms are trained for specific document formats, then extraction accuracy for that format is improved, but the system cannot analyze documents with different formats
Solution Approach 1:
The patent implements a universal information extraction system that can process multiple document formats (tables, paragraphs, graphics) using a single trained NLP model. The model is trained on diverse document types and formats, enabling it to adaptively extract information from various structures without requiring separate models for each format, thus achieving both accuracy and versatility
2Loss of information
If information is extracted from all documents manually, then complete information extraction is achieved, but labor costs and time consumption increase significantly
Solution Approach 1:
The system implements automated self-service information extraction using trained NLP algorithms that can independently identify and extract relevant information from documents without human intervention. The model automatically processes documents, identifies points of interest, and extracts data, eliminating the need for manual extraction while maintaining completeness and significantly reducing processing time
3Loss of information
If all document information is stored in memory, then complete data availability is maintained, but memory usage increases significantly
Solution Approach 1:
The system extracts only the essential and relevant information from documents using the NLP model, storing only the extracted data points rather than complete document content. This selective extraction maintains data availability for analysis while significantly reducing memory consumption by eliminating redundant information such as formatting, whitespace, and non-essential text
Data Source
AI summary
A method of automatically extracting information from electronic documents is discussed. The method includes a computer system receiving a plurality of electronic documents of a particular type that includes information arranged in a plurality of different formats. The method further includes, for each of a set of electronic documents, the computer system analyzes the electronic documents to identify tokens within the electronic documents, identifies a plurality of points-of-interest within the electronic documents, and matches points-of-interest based on distance between points-of-interest and a determination by a natural language processing model that the points-of-interest correspond. The method further includes generating revised versions of the electronic documents in which the matched points-of-interest are arranged in a universal format and storing the revised versions of the electronic documents.