Natural Language Processing Style Information Extraction
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Solution Overview
Problem
Existing natural language processing technologies primarily rely on semantic information and fail to effectively incorporate style information, such as text color, size, style, and font, to infer important information in documents.
Innovation Solution
A machine learning-based apparatus and method that collects documents with style information, extracts and labels this information, and uses a first machine learning module to predict the position of words with style information, while a second module summarizes text content based on the style-related embedding vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional deep learning language models are used for text analysis, then semantic information can be processed, but style information (text color, size, style, font) is ignored and important content cannot be effectively identified
Solution Approach 1:
The patent merges style information processing with semantic information processing by integrating style feature extraction into the existing deep learning language model framework. The style information (color, size, font, etc.) is combined with semantic embeddings to create enriched representations, allowing the model to process both types of information simultaneously without requiring completely separate systems.
Solution Approach 2:
The patent creates a multi-functional processing system that can handle both traditional semantic analysis and style information analysis within a single framework. The model is designed to process diverse document features (semantic content, stylistic attributes, positional information) through unified embedding layers and attention mechanisms, making the system versatile for various text analysis tasks.
2Measurement precision
If only semantic information is used for text analysis, then processing is simpler, but important content identification accuracy is reduced
Solution Approach 1:
The patent performs preliminary extraction and encoding of style information before the main analysis process. Style features (color, size, font characteristics) are pre-processed and converted into numerical representations that can be seamlessly integrated with semantic embeddings. This preliminary preparation enables the model to access style information efficiently during content identification without adding significant computational overhead during inference.
Solution Approach 2:
The patent adds a new dimension to text analysis by incorporating style information as an additional feature space. Instead of analyzing only semantic content, the model now processes documents in multiple dimensions: semantic meaning, stylistic attributes, and their interactions. This multi-dimensional approach enriches the representation space, enabling more accurate identification of important content through attention mechanisms that can weigh different dimensions differently.
Data Source
AI summary
An apparatus for processing natural language includes a collection module for collecting a document having style information on text in the document, a preprocessing module for extracting style information from the text of the collected document, and labeling the extracted style information with the text, and a first machine learning module receiving the text labeled with the style information, and predicting a position of a word having the style information in the received text.