Document Classification Models Using Clustering and Transfer Learning
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Solution Overview
Problem
Existing data management systems struggle with high accuracy, speed, and low computing cost for categorizing and classifying large volumes of unstructured data, while also needing to be adaptable to diverse organizational requirements and scalable across different jurisdictions.
Innovation Solution
A deep learning engine with a feature extraction module and classification module that uses neural networks for context and content-aware data classification, combined with a smart sampling module for efficient document selection and a transfer learning approach to minimize labeled data requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data classification methods are used, then computing cost and training time are reduced, but prediction accuracy and scalability deteriorate
Solution Approach 1:
The system segments the data classification task into multiple components: feature extraction module that identifies key characteristics, clustering module that groups similar documents, and a streamlined classification model. This segmentation allows each component to be optimized independently, achieving high accuracy through sophisticated feature analysis while maintaining fast execution through efficient modular processing.
Solution Approach 2:
The system performs preliminary feature extraction and document clustering before the actual classification prediction. By pre-processing documents to extract relevant features and group similar items together, the system reduces the computational burden during inference, enabling both high accuracy predictions and fast execution speed.
2Measurement precision
If comprehensive feature extraction is performed, then classification accuracy is improved, but computing cost and processing time increase
Solution Approach 1:
The system extracts only the most relevant features from documents using the feature extraction module, rather than processing all possible attributes. The clustering module further refines this by identifying representative documents from each group. This selective extraction maintains high classification accuracy while significantly reducing computing cost by focusing only on essential features.
Solution Approach 2:
The system applies different processing depths to different documents based on their characteristics. The feature extraction module identifies and focuses computational resources on key features specific to each document type, while the clustering module groups documents to share computational processing. This local quality approach ensures accurate classification without uniformly high computing cost across all documents.
3Adaptability or versatility
If a fixed classification model is used, then implementation simplicity is maintained, but adaptability to different organizational requirements deteriorates
Solution Approach 1:
The system employs dynamic, trainable classification models that can adapt to different organizational requirements. The neural network-based classifier is designed to be retrained with organization-specific data, allowing the system to adapt its classification behavior. The feature extraction and clustering modules also adjust their parameters based on the specific domain, providing versatility without requiring complete system redesign for different organizations.
Solution Approach 2:
The system uses universal components that can serve multiple functions across different organizations. The feature extraction module, clustering module, and classification framework are designed as reusable, configurable components that can be adapted to various organizational needs through parameter adjustment and retraining, rather than requiring organization-specific implementations of each component.
4Measurement precision
If large labeled datasets are used for training, then model accuracy is improved, but training time and data preparation effort increase
Solution Approach 1:
The system performs preliminary clustering of documents before the classification training phase. By grouping similar documents together in advance, the system identifies representative samples from each cluster that can be used for training. This preliminary action reduces the amount of labeled data needed while maintaining model accuracy, as the clustering structure provides implicit organization that aids learning.
Solution Approach 2:
The system uses the clustered structure and feature representations as templates or copies that can be reused across different training scenarios. Once the feature extraction and clustering are performed on a dataset, these representations can be copied and applied to train classification models for different organizations or document types, reducing the need to recreate the entire training pipeline from scratch.
Data Source
AI summary
A method for document classification is described. A first dataset of labeled corporate data, a second dataset of internal labeled documents for a customer, and a third dataset of unlabeled documents for the customer are obtained. A classification model is trained using the first dataset. The classification model is further trained using the second dataset. Feature extraction is performed on each of the unlabeled documents of the third dataset by vectorizing content and metadata of each unlabeled document into one or more vectors and concatenating the one or more vectors to obtain a fixed length vector. Each of the unlabeled documents of the third dataset is clustered into one or more clusters based on similarity between the fixed length vectors for each unlabeled document. The unlabeled documents in each of the clusters are automatically labeled using text summarization. The classification model is retrained using the automatically labeled documents.