Feature Extraction Language for Document Corpus Configuration
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Solution Overview
Problem
Custom designing applications for feature extraction in natural language processing is time-consuming, error-prone, and resource-intensive, requiring specialized knowledge in specific fields and machine learning applications, making it difficult to generate and utilize feature vectors effectively.
Innovation Solution
A feature extraction language is used to configure and perform feature extraction on documents, generating a feature vector that includes identification information, reducing the complexity of generating feature vectors and improving compatibility, and minimizing resource usage by automating the configuration of parameters based on stored information or user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If custom applications are designed for feature extraction, then feature extraction can be performed, but the process becomes time-consuming, error-prone, and resource-intensive
Solution Approach 1:
The patent creates a universal feature extraction system that can handle multiple document types and extraction scenarios through a single platform. The system uses configurable parameters and stored information to adapt to different feature extraction needs without requiring custom application design, thereby reducing time loss while maintaining reliability across diverse use cases.
Solution Approach 2:
The system automatically configures feature extraction parameters by utilizing stored information about the document corpus and extraction requirements. This self-service capability eliminates the need for manual, error-prone configuration by domain experts, reducing both time consumption and errors while maintaining extraction accuracy through automated parameter optimization.
2Adaptability or versatility
If custom applications are designed for feature extraction, then specialized feature extraction can be achieved, but resource consumption increases
Solution Approach 1:
The system performs preliminary configuration of feature extraction parameters by storing information about document characteristics and extraction requirements in advance. This pre-processing step enables the system to quickly adapt to different feature extraction tasks without consuming excessive resources during actual execution, maintaining versatility while optimizing resource usage.
Solution Approach 2:
The system achieves adaptability for different feature extraction scenarios by dynamically changing extraction parameters rather than designing custom applications. Configuration parameters are adjusted based on stored information about the corpus and requirements, enabling versatile feature extraction across document types while minimizing processing resource consumption through parameter optimization instead of custom implementation.
3Ease of operation
If manual configuration of feature extraction parameters is performed, then extraction can be customized, but the process becomes complex and error-prone
Solution Approach 1:
The system automatically configures feature extraction parameters by utilizing stored information about the document corpus and extraction requirements. This self-service capability eliminates the need for manual, error-prone configuration by domain experts, reducing both time consumption and errors while maintaining extraction accuracy through automated parameter optimization.
4Use of energy by moving object
If a standardized feature extraction system is used, then resource efficiency improves, but compatibility across different applications may be limited
Solution Approach 1:
The patent creates a universal feature extraction system that can handle multiple document types and extraction scenarios through a single platform. The system uses configurable parameters and stored information to adapt to different feature extraction needs without requiring custom application design, thereby reducing time loss while maintaining reliability across diverse use cases.
Solution Approach 2:
The system achieves adaptability for different feature extraction scenarios by dynamically changing extraction parameters rather than designing custom applications. Configuration parameters are adjusted based on stored information about the corpus and requirements, enabling versatile feature extraction across document types while minimizing processing resource consumption through parameter optimization instead of custom implementation.
Data Source
AI summary
A device may receive a first command, included in a set of commands, to set a configuration parameter associated with performing feature extraction. The device may receive a second command, included in the set of commands, to set a corresponding value for the configuration parameter. The configuration parameter and the corresponding value may correspond to a particular feature metric that is to be extracted. The device may configure, based on the configuration parameter and the corresponding value, feature extraction for a corpus of documents. The device may perform, based on configuring feature extraction for the corpus, feature extraction on the corpus to determine the particular feature metric. The device may generate a feature vector based on performing the feature extraction. The feature vector may include the particular feature metric. The feature vector may include a feature identifier identifying the particular feature metric. The device may provide the feature vector.