Bayesian Augmented Feature Extraction for Document Classification
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Solution Overview
Problem
Existing text classification methods are inadequate in handling spatial and structural features of documents, as they typically treat texts as 'bags of words' without considering positional and format characteristics, limiting their ability to accurately classify documents with complex structures.
Innovation Solution
A system that employs Bayesian analysis to identify and extract augmented features, including local, sectional, and document-level features, from text documents, which extends beyond standard feature extraction by incorporating format and structural characteristics, enabling more accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional bag of words feature extraction is used, then the feature extraction process is simple, but spatial and structural features of documents are lost
Solution Approach 1:
The document is segmented into multiple hierarchical levels (document-level, section-level, local features) rather than treating it as a flat bag of words. This segmentation preserves spatial and structural information by analyzing features at different granularities and positions within the document hierarchy.
Solution Approach 2:
The patent adds spatial and structural dimensions to feature extraction by incorporating document orientation, color, positional characteristics, and hierarchical structure. This transforms the feature space from one-dimensional (word frequencies) to multi-dimensional, capturing both content and structural properties.
2Measurement precision
If Bayesian analysis is applied to identify augmented features, then text classification accuracy is improved, but computational complexity increases
Solution Approach 1:
Bayesian analysis is applied in advance to identify which augmented features are most relevant for classification before the actual classification process. This preliminary feature selection step reduces the dimensionality of the feature space, thereby reducing computational complexity in subsequent classification operations while maintaining high accuracy.
Solution Approach 2:
The system automatically performs Bayesian analysis to self-determine which features to extract and how to weight them, eliminating the need for manual feature engineering. This automation reduces computational overhead by focusing only on the most informative features rather than processing all possible features.
Data Source
AI summary
Identification of augmented features based on a Bayesian analysis of a text document is disclosed. One example is a system including a document processing module, a feature processing module, and a feature generation module. The document processing module receives a text document via a processor. The feature processing module automatically identifies, based on a Bayesian analysis of the text document, a plurality of augmented features in the text document, the plurality of augmented features including at least one of local, sectional, and document-level features of the text document, and extracts, via the processor, the identified plurality of augmented features from the text document. The feature generation module generates, via the processor, a feature representation of the text document based on the extracted plurality of augmented features.