File Information Extraction Using Global Pointer and Semantic Matching
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Solution Overview
Problem
Existing methods for acquiring information from files, such as text documents, are inefficient and prone to errors due to file size limitations and lack of effective recognition of candidate information.
Innovation Solution
A method utilizing a Global Pointer model for parallel processing and a pre-trained matching model to recognize candidate information, followed by determining target recognition and semantic features to identify accurate information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If named entity recognition method and reading comprehension method are used to acquire key information from files, then information extraction can be performed, but the processing efficiency deteriorates and accuracy decreases when file size increases
Solution Approach 1:
The patent segments the file processing task into multiple stages: first extracting text content from the file, then performing named entity recognition to identify candidate information, and finally using reading comprehension to determine target information. This segmentation allows each stage to be optimized independently, improving overall efficiency while maintaining accuracy even for large files.
Solution Approach 2:
The patent performs preliminary text extraction and candidate information identification before the final information extraction step. By pre-processing the file to identify potential candidate information using named entity recognition, the system reduces the search space for subsequent reading comprehension, thereby improving processing efficiency without compromising accuracy.
2Reliability
If traditional information extraction methods are used, then processing can be performed, but the system cannot effectively handle large files and is prone to errors
Solution Approach 1:
The patent introduces an intermediary reading comprehension model that acts as a mediator between the raw text and the final extracted information. This intermediary layer validates and verifies the candidate information identified by named entity recognition, reducing errors and improving reliability. The system can handle files of various sizes by adjusting the processing parameters of this intermediary layer.
Solution Approach 2:
The patent dynamically adjusts processing parameters such as candidate information thresholds and reading comprehension depth based on file size and content complexity. For larger files, the system optimizes parameters to balance processing time and accuracy, thereby improving both error resistance and file size adaptability.
3Productivity
If parallel processing with Global Pointer model is implemented, then processing speed improves, but system complexity increases
Solution Approach 1:
The patent uses the Global Pointer model to create multiple candidate information copies from the text, which are then processed in parallel through reading comprehension. This copying approach enables parallel processing that speeds up information extraction while the model's architecture manages the complexity by providing a structured framework for handling multiple candidates systematically.
Data Source
AI summary
Disclosed are a method for acquiring information. The method includes: acquiring a file to be processed and an information type; recognizing at least one piece of candidate information related to the information type from the file to be processed; determining a target recognition feature and a semantic feature of each piece of candidate information, the target recognition feature is configured to describe a matching condition between the each piece of candidate information and the information type; and determining target information from the at least one piece of candidate information based on the target recognition feature and the semantic feature.