Transformer Model for Automatic Document Structure Identification
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Solution Overview
Problem
Conventional text editors are unable to automatically identify structural entities in documents from plain text, requiring users to manually convert text into headings, paragraphs, or list elements, which is inefficient and time-consuming.
Innovation Solution
A text processing apparatus using a machine learning model with a transformer-over-transformer structure that generates entity embeddings and style information for plain text, allowing for automatic identification and formatting of structural classes such as headings, paragraphs, and lists without relying on document structure annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional text editors are used for document formatting, then users can manually style text, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs automatic structure identification and styling without requiring user intervention. The machine learning model autonomously analyzes plain text, identifies structural entities (headings, paragraphs, lists), and applies appropriate formatting styles, allowing the document to style itself rather than requiring manual user formatting.
Solution Approach 2:
The patent replaces the mechanical manual formatting process with an automated machine learning-based system. Instead of users manually selecting and applying styles, the system uses neural networks and transformer models to automatically detect document structure and apply formatting, substituting human mechanical action with computational automation.
2Productivity
If automatic structure identification is implemented, then formatting efficiency improves, but system complexity increases
Solution Approach 1:
The system divides the complex document structure identification task into multiple specialized components: a transformer model for sequence encoding, an entity recognition module for identifying structural elements, and a style application module for formatting. This segmentation allows each component to specialize in a specific aspect, managing overall system complexity through modular architecture.
Solution Approach 2:
The patent introduces entity embeddings as an intermediary representation between the input plain text and the final styled output. The transformer model converts text into entity embeddings that capture structural information, which then serve as an intermediate representation for the styling decision-making process, simplifying the overall transformation pipeline.
3Measurement precision
If manual text styling is performed, then formatting accuracy can be controlled, but the process requires significant user effort
Solution Approach 1:
The system employs trained machine learning models that have learned from labeled training data to accurately identify document structures. The model receives feedback during training through loss functions that compare predicted structures with ground truth annotations, enabling it to achieve high accuracy in identifying headings, paragraphs, and lists without requiring user correction.
Solution Approach 2:
The automatic styling system performs structure identification and formatting application autonomously without requiring user interaction. The machine learning model independently analyzes the text content, determines structural boundaries, and applies appropriate styles, completely eliminating the need for manual user effort in the formatting process while maintaining high accuracy.
Data Source
AI summary
Systems and methods for natural language processing are described. Embodiments of the present disclosure receive plain text comprising a sequence of text entities; generate a sequence of entity embeddings based on the plain text, wherein each entity embedding in the sequence of entity embeddings is generated based on a text entity in the sequence of text entities; generate style information for the text entity based on the sequence of entity embeddings; and generate a document based on the style information.