Iterative Fact Extraction Using Multi-Level Document Tagging
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Solution Overview
Problem
Existing search engines and indexers struggle to efficiently extract relevant information from documents, requiring users to spend significant time processing retrieved documents to derive answers for their queries.
Innovation Solution
A novel iterative fact-extraction method that analyzes documents to identify patterns of document elements and tags, associating them with facts through a multi-level process, using pattern analysis instructions to enhance efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional search engines use web crawlers and indexers to retrieve and parse documents, then documents can be retrieved and indexed, but users still need to spend significant time processing the retrieved documents to derive answers
Solution Approach 1:
The system performs preliminary fact extraction and pattern recognition during the indexing phase, identifying and annotating factual patterns in documents before users query the system. This preliminary analysis of document structures, relationships, and factual patterns enables the system to pre-process information in advance, reducing the time users need to spend on manual document processing while maintaining high accuracy through structured pattern recognition
Solution Approach 2:
The system introduces an intermediary layer of semantic analysis and pattern recognition between traditional keyword indexing and user queries. This intermediary layer includes structured fact extraction, relationship identification, and pattern annotation that transforms unstructured document content into organized factual representations, enabling accurate information retrieval without requiring users to manually process raw documents
2Productivity
If indexers parse each sentence to generate grammatical trees and keywords, then document structures can be analyzed, but the system cannot efficiently extract relevant facts and patterns
Solution Approach 1:
The system extracts specific factual patterns and relationships from parsed document structures, separating meaningful factual information from general grammatical analysis. By identifying and extracting structured facts, entities, and relationships from grammatical trees and parsed content, the system efficiently captures essential factual information while discarding redundant grammatical details, improving productivity without losing critical factual data
Solution Approach 2:
The system performs preliminary pattern recognition and fact identification during the indexing phase, analyzing document structures, identifying factual patterns, and annotating relationships before queries are submitted. This advance processing of document content into structured factual representations enables efficient fact extraction during query processing while preserving complete factual information for future retrieval
3Measurement precision
If the system performs multi-level iterative analysis to identify patterns and associate tags with facts, then fact extraction accuracy improves, but processing complexity increases
Solution Approach 1:
The system segments the complex analysis process into distinct iterative stages, each focusing on specific pattern types and factual relationships. By dividing the fact extraction process into multiple passes with increasing specificity - from basic pattern recognition to complex relationship identification - the system achieves high accuracy while managing complexity through modular, staged processing rather than attempting all analyses simultaneously
Solution Approach 2:
The system employs dynamic pattern recognition where analysis rules and patterns are applied iteratively with increasing sophistication. Each iteration builds upon previous results, adapting the analysis depth and pattern complexity based on the structured data generated in earlier passes. This dynamic approach allows the system to achieve high measurement precision through progressive refinement while controlling device complexity by only applying complex analyses when and where needed
Data Source
AI summary
Some embodiments provide a method for identifying a first pattern formed by a first set of document elements. The method associates a tag that identifies the first pattern with the document. The method then identifies a second pattern formed by a second set of document elements and the tag. The method associates a second tag with the document. The second tag identifies the second pattern and is associated with a fact. Some embodiments provide a graphical user interface (GUI) for manually processing tags associated with a document. Further, different embodiments provide a system for performing iterative fact-extraction on a set of documents.