Prompt-Learned Information Extraction and Image Reordering for Noise Reduction
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Solution Overview
Problem
Existing methods for extracting information from textual images face challenges such as the difficulty in generating pre-structured information templates, labor-intensive processes, and a lack of semantic relationship between key information and corresponding keys, leading to noise and diminished accuracy.
Innovation Solution
A method using a semantic encoder of a pre-trained NLP model to generate semantic vectors that encode discrete portions of text, determining semantic similarity based on document positions, and re-training the model to enhance NLP capabilities for key-value pair generation and logical relationship prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a pre-structured information template is used to extract text from textual images, then the extraction process can be automated, but the template generation becomes labor intensive and requires manual modification whenever text style changes
Solution Approach 1:
The system uses a pre-trained NLP model that automatically adapts to different document styles and structures without requiring manual template creation or modification. The model self-adjusts to extract information accurately regardless of text formatting changes, eliminating the need for human intervention in template maintenance.
Solution Approach 2:
Instead of using fixed templates with rigid structural parameters, the system employs a flexible NLP model that dynamically adjusts its processing parameters based on the input document characteristics. This allows the system to handle varying text styles, layouts, and formats automatically.
2Productivity
If all text portions are identified and processed to generate key-value pairs, then comprehensive information extraction is achieved, but noise from useless text increases and accuracy diminishes
Solution Approach 1:
The system segments text into discrete portions and uses semantic similarity scoring to distinguish between useful and useless text. By evaluating each segment's semantic relevance to potential key-value pairs, the system filters out noise while maintaining comprehensive extraction capability.
Solution Approach 2:
The pre-trained NLP model acts as an intermediary that processes raw text and transforms it into semantically enriched representations. This intermediary layer enables the system to distinguish meaningful information from noise by leveraging the model's pre-learned linguistic understanding and contextual relationships.
Data Source
AI summary
Information extraction and image restructuring includes generating semantic vectors to encode portions of text extracted from a document. For each semantic vector a semantic similarity between a schema key and other text encoded therein is determined based on their respective positions within the document. An enhanced NLP model is created using the semantic vectors, each labeled according to the semantic similarity. The text, including schema key, are re-encoded as a key and candidate vectors. Key-value pairs are generated by matching the key vector with a predetermined number of candidate vectors. The enhanced NLP model, using prompt learning, is repurposed to perform a next-sentence prediction that predicts which of the candidate vectors is logically related to the schema key. Based on the next-sentence prediction, the discrete portion of text identified as the schema key and portion of text determined to be logically related thereto are output.