Unstructured Text Fact Extraction via Detection and Annotation
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Solution Overview
Problem
Existing solutions face challenges in efficiently identifying and understanding objects within unstructured natural language text and processing content changes, making it difficult to extract qualitative and quantitative data effectively.
Innovation Solution
A computer system and method utilizing an AI platform with a machine learning manager and data manager to annotate unstructured natural language text, identify mentions, and extract facts by applying detection and fact aggregator models, converting unstructured data into structured format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to identify and extract data from unstructured text, then accuracy can be maintained, but productivity is significantly reduced
Solution Approach 1:
The patent introduces an AI platform as an intermediary between unstructured text and structured data extraction. The platform includes a detection model that identifies mentions and their positions, a fact aggregator model that extracts facts based on patterns, and a data manager that converts facts to structured data. This intermediary system automates the extraction process while maintaining accuracy through multiple processing stages.
Solution Approach 2:
The patent segments the data extraction process into distinct modular components: (1) detection model for identifying mentions and positions, (2) annotation process for labeling mentions, (3) fact aggregator model for extracting facts based on patterns, and (4) data manager for converting to structured format. This segmentation allows each component to specialize in specific tasks, improving overall efficiency and maintainability.
2Adaptability or versatility
If complex AI models are used to extract facts from unstructured text, then extraction capability is improved, but device complexity increases
Solution Approach 1:
The complex extraction task is divided into separate specialized models: a detection model for identifying mentions, an annotation system for labeling, and a fact aggregator model for extracting facts. Each model has a specific function, reducing the complexity of individual components while maintaining high overall extraction capability through their coordinated operation.
Solution Approach 2:
The patent introduces intermediate processing layers including an annotation system that bridges detection and fact aggregation. The annotation process creates structured intermediate representations that simplify the subsequent fact extraction task, making the overall system more manageable despite its sophisticated capabilities.
3Productivity
If automated processing is applied to unstructured text, then productivity increases, but measurement precision of extracted data decreases
Solution Approach 1:
The automated processing is segmented into multiple sequential stages: detection of mentions, annotation with labels and positions, pattern matching by the fact aggregator, and final conversion to structured data. This multi-stage approach allows automated processing at high speed while maintaining precision through progressive refinement at each stage.
Solution Approach 2:
The system employs feedback mechanisms where the detection model's outputs are refined by the annotation process, which in turn provides improved inputs to the fact aggregator. The data manager provides feedback on extraction results, enabling continuous improvement of extraction accuracy while maintaining automated processing speeds.
Data Source
AI summary
A system, computer program product, and method are provided for extraction of factual data from unstructured natural language (NL) text. A detection model is applied to convert unstructured NL text in a first language to annotated NL text. The detection model identifies two or more mentions from the unstructured NL text and a logical position of the mentions. The detection model further identifies a sequential position for each of the mentions and attaches a sequential position identifier. A pattern of rules corresponding with the annotated NL text is identified and applied to the annotated NL text, and one or more facts embedded within the annotated NL text are extracted and converted into structured data.