Segmentation and Labeling Model for Document Metadata
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Solution Overview
Problem
Existing machine-learning techniques for segmenting and labeling documents often result in inaccurate or ineffective grouping of text segments, leading to degraded performance in software tools that rely on structured document metadata for editing and navigation.
Innovation Solution
A machine-learning tool using a segmentation-and-labeling model with a segmentation network and a pooling network to jointly segment and label text, generating metadata that identifies segments and topics within documents, thereby improving the accuracy of text processing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional machine-learning techniques are used for document segmentation, then the process can be automated, but the segmentation accuracy and topic labeling precision deteriorate
Solution Approach 1:
The patent applies segmentation by dividing the document processing task into distinct components: a segmentation network that identifies segment boundaries and a pooling network that generates topic labels for each segment. This multi-component approach allows each network to specialize in its specific function, improving overall accuracy while maintaining automation.
Solution Approach 2:
The patent introduces encoded sentence representations as an intermediary between the input text and the segmentation/topic labeling processes. These encoded representations serve as a bridge that captures semantic information, enabling more accurate segmentation decisions and topic label generation while maintaining automated processing.
2Productivity
If traditional machine-learning techniques are used for topic labeling, then processing speed is maintained, but the precision of topic identification deteriorates
Solution Approach 1:
The patent maintains continuous processing by having the segmentation network and pooling network operate in a streamlined sequence without interruption. The encoded sentence representations are generated once and reused by both networks, eliminating redundant processing while maintaining high topic labeling accuracy through the specialized pooling network.
3Device complexity
If separate models are used for segmentation and topic labeling, then model complexity is reduced, but the overall system performance deteriorates
Solution Approach 1:
The patent merges the segmentation and topic labeling functions into a unified system where both networks process the same encoded sentence representations. This integration allows the networks to work together synergistically, with the segmentation network providing structured boundaries that enhance topic labeling accuracy, while maintaining manageable complexity through shared encoding infrastructure.
Data Source
AI summary
Certain embodiments involve using a machine-learning tool to generate metadata identifying segments and topics for text within a document. For instance, in some embodiments, a text processing system obtains input text and applies a segmentation-and-labeling model to the input text. The segmentation-and-labeling model is trained to generate a predicted segment for the input text using a segmentation network. The segmentation-and-labeling model is also trained to generate a topic for the predicted segment using a pooling network of the model to the predicted segment. The output of the model is usable for generating metadata identifying the predicted segment and the associated topic.