Relation Extraction Using Spatial Layout in Semi-Structured Documents
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Solution Overview
Problem
Conventional relationship extraction methods struggle with semi-structured documents, failing to utilize spatial and visual properties that enhance accuracy and limiting extensibility.
Innovation Solution
Leveraging spatial and visual properties from semi-structured documents to augment entity recognition and relationship extraction, using models trained with annotated user data to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional relationship extraction methods are used on semi-structured documents, then the extraction process is simple, but the accuracy is low due to failure to utilize spatial and visual properties
Solution Approach 1:
The patent transitions from traditional text-only relationship extraction to a multi-dimensional approach that incorporates spatial layout data and visual properties (such as font size, color, and formatting) as additional dimensions of information. This allows the extraction model to leverage both textual content and document structure, significantly improving accuracy on semi-structured documents while managing complexity through integrated processing pipelines.
2Measurement precision
If spatial and visual properties are incorporated to enhance accuracy, then relationship extraction accuracy improves, but the system complexity increases
Solution Approach 1:
The patent merges multiple information sources (textual content, spatial layout coordinates, visual formatting properties) into a unified relationship extraction framework. By combining these diverse data types into a single processing system that jointly analyzes all features, the patent achieves high accuracy without creating separate complex subsystems, thus managing overall system complexity while leveraging multiple dimensions of information.
3Adaptability or versatility
If models are trained with annotated user data for specific document types, then extensibility and customization improve, but training time and data requirements increase
Solution Approach 1:
The patent implements preliminary action by pre-training relationship extraction models on large corpora of annotated semi-structured documents across multiple document types. This pre-training establishes a strong foundation that can be quickly adapted to specific document types through fine-tuning with smaller, targeted datasets. The preliminary training phase captures general patterns in spatial and visual properties, reducing the time and data needed for subsequent customization while maintaining high extensibility to new document types.
Data Source
AI summary
Systems and methods are provided for generating a set of entity data items using an entity recognition model and text of a document, wherein a first entity data item of the set of entity data items represents a first entity mention in the text, and wherein a second entity data item of the set of entity data items represents a second entity mention in the text; generating augmentation data regarding one or more layout properties of the text; generating a set of relation data items using the set of entity data items, the augmentation data, and a relation detection model, where a first relation data item of the set of relation data items represents a relation between the first entity mention and the second entity mention; and generating, using the set of relation data items, a user interface configured to present relations between entities in the text.