NLP Entity Identification via Feature Extraction
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Solution Overview
Problem
The manual identification of input entities in text documents is a time-consuming, complex, and error-prone process, especially in software development and testing phases, as it requires precise understanding of system requirements and often involves excessive manual work.
Innovation Solution
A device and method utilizing natural language processing and machine learning to identify input entities by performing feature extraction techniques such as determining tag patterns, capitalization, headwords, constituent words, semantic similarities, and surrounding context, thereby automating the identification process and reducing manual effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of input entities is performed, then precision in understanding system requirements is improved, but time consumption and complexity increase significantly
Solution Approach 1:
The patent introduces an intermediary system comprising NLP processors and machine learning models that act as a mediator between the requirement text and the final entity identification. This intermediary automatically extracts and classifies input entities, maintaining high precision while eliminating manual analysis time consumption.
Solution Approach 2:
The patent replaces the mechanical manual process of entity identification with an automated computational system using natural language processing and machine learning algorithms. This substitution maintains measurement precision through sophisticated language understanding while dramatically reducing time loss by eliminating human manual intervention.
2Measurement precision
If manual identification of input entities is performed, then accuracy in entity classification is improved, but device complexity and operational complexity increase
Solution Approach 1:
The patent segments the complex identification task into distinct processing stages: text preprocessing, entity extraction, classification, and validation. Each stage is handled by specialized NLP processors and machine learning models, which maintains high classification accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The patent employs intermediary NLP processors and feature extraction modules that bridge the gap between raw text and classified entities. These intermediaries handle the computational complexity internally while presenting a simplified interface, maintaining accuracy without exposing the full system complexity.
3Productivity
If automated feature extraction techniques are used, then processing speed is improved, but computational resource consumption increases
Solution Approach 1:
The patent applies partial feature extraction by selecting only the most relevant features for entity classification rather than processing all possible text attributes. This approach maintains high processing speed by focusing computational resources on critical features while reducing overall resource consumption through selective analysis.
Solution Approach 2:
The patent dynamically adjusts processing parameters such as feature extraction depth, model complexity, and analysis granularity based on input characteristics. This allows the system to maintain high productivity when needed while conserving computational resources during routine operations, effectively balancing speed and resource usage.
Data Source
AI summary
A device may include one or more processors. The device may receive text to be processed to identify input entities included in the text. The device may identify text sections of the text. The device may generate a list of terms included in the text sections of the text. The device may perform one or more feature extraction techniques, on the terms included in the text sections, to identify the input entities included in the text. The device may generate information that identifies the input entities included in the text, based on performing the one or more feature extraction techniques. The device may provide the information that identifies the input entities included in the text.