Automated Data Classification via Semantic Feature Extraction
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Solution Overview
Problem
Existing technologies face challenges in extracting structured data from unstructured documents, such as PDFs and images, for semantic search, as they lack semantic annotations, making it difficult to classify content like restaurant menus effectively.
Innovation Solution
A system that identifies and classifies tokens from unstructured content sources, generates features, and trains classifiers using known-good data to determine the type and classification of input documents, enabling automated data management and updating of content like restaurant menus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic search techniques are applied to unstructured documents, then search relevance is improved, but the ability to extract structured data is worsened due to lack of semantic annotations
Solution Approach 1:
The system performs preliminary actions by automatically extracting and annotating semantic information from unstructured documents before classification. The training crawler pre-processes documents to identify entities, relationships, and semantic patterns, creating structured representations that enable subsequent classification and search operations without requiring pre-existing annotations.
Solution Approach 2:
The patent introduces an intermediary layer of semantic representation that mediates between unstructured documents and classification systems. This intermediary takes the form of extracted feature vectors, entity graphs, and semantic embeddings that translate raw unstructured content into a format suitable for machine learning classifiers while preserving semantic meaning.
2Measurement precision
If automated classification systems are trained on known-good data, then classification accuracy is improved, but the complexity of data preparation is worsened
Solution Approach 1:
The training crawler performs self-service by automatically generating training data from unstructured sources without requiring manual annotation. The system self-suffices by extracting semantic information, creating feature representations, and assembling training datasets autonomously, eliminating the need for human annotators while maintaining data quality through automated validation mechanisms.
Solution Approach 2:
The system transforms the data preparation process by changing key parameters: instead of requiring manually annotated training data, the system automatically generates features and labels from unstructured documents. This parameter change involves converting raw text into structured feature vectors and transforming unlabelled documents into labelled training sets through automated semantic extraction.
3Ease of operation
If unstructured documents are processed without semantic annotations, then processing simplicity is maintained, but information extraction capability is worsened
Solution Approach 1:
The patent extracts semantic information directly from unstructured documents by identifying and isolating key entities, relationships, and patterns. The training crawler extracts meaningful features such as product names, prices, descriptions, and categorical information, separating these semantic elements from the raw unstructured text to enable downstream processing while preserving information integrity.
Data Source
AI summary
A system and method for data classification are presented. A plurality of training tokens are identified by at least one server communicatively coupled to a network. Each training token includes a token retrieved from a content source and a classification of the token. For each training token in the plurality of training tokens, a plurality of n-gram sequences are identified, a plurality of features for the plurality of n-gram sequences are generated, and first training data is generated using the token retrieved from the content source, the plurality of features, and the classification of the token. A first classifier is trained with the first training data, and the first classifier is stored into a storage system in communication with the at least one server.