Document Visual Structure for More Accurate Question Answering
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Solution Overview
Problem
Existing question-answering systems fail to effectively utilize the visual structure of documents for enhancing information retrieval, leading to incomplete or inaccurate answers due to the lack of consideration of semantic relationships between document elements.
Innovation Solution
The system extracts and incorporates structural information from the visual layout of documents by segmenting and classifying visual elements, establishing context relationships, and integrating this information into a knowledge base to improve the accuracy and comprehensiveness of answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system uses only text content for question answering, then the processing is simpler and faster, but the accuracy and comprehensiveness of answers deteriorates due to loss of visual structural information
Solution Approach 1:
The patent segments documents into visual units (blocks, lines, words, characters) and processes their spatial relationships separately from text content. This segmentation allows the system to extract visual structural information without completely redesigning the entire processing pipeline, thus improving answer accuracy while controlling complexity growth.
Solution Approach 2:
The patent introduces visual structural information as an intermediary layer between raw text and question-answering processing. This intermediary captures spatial relationships and visual hierarchy, enabling more accurate answer retrieval without directly complicating the core text-processing mechanisms.
2Reliability
If the system extracts and processes visual structural information, then the accuracy and comprehensiveness of answers improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs visual structure extraction and spatial relationship analysis during the document ingestion phase, before question-answering operations begin. This preliminary action pre-computes visual structural information that can be reused across multiple queries, reducing processing time for subsequent operations while maintaining comprehensive answer quality.
Solution Approach 2:
The patent applies different processing strategies to different visual elements based on their specific characteristics and relevance to question answering. Not all visual structural information is processed with the same depth or detail, allowing the system to optimize processing time by focusing computational resources on the most relevant visual features.
Data Source
AI summary
A question-answering system that receive a natural-language question includes a database to provide a basis for that answer and a structured-query generator that constructs a structured query from the question and uses it to obtain an answer to the question from the database.