Automated Text Classification via Cosine Similarity
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Solution Overview
Problem
Manual annotation of large datasets for training deep learning models is time-consuming, expensive, and prone to errors, especially when experts are not available to label feedback effectively for text classification tasks.
Innovation Solution
A method using statistical methods to automatically label user feedback by generating machine-readable vectors from sample keywords and phrases, employing cosine similarity scores to classify feedback into categories without human intervention, allowing for efficient training of neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to label training data, then classification accuracy can be maintained, but time consumption and cost increase significantly
Solution Approach 1:
The system performs self-service by automatically generating labels through statistical methods and cosine similarity calculations without requiring human annotators. The feedback is labeled by comparing it with sample feedback using vector similarity, enabling the system to annotate its own training data at scale.
Solution Approach 2:
The patent replaces the mechanical human annotation process with an automated computational system. Instead of manual reading and classification by experts, the system uses statistical methods, vector representations, and cosine similarity algorithms to automatically generate labels, eliminating the need for human labor in the annotation process.
2Reliability
If manual annotation is used to label training data, then label quality can be ensured, but cost increases significantly
Solution Approach 1:
The system performs self-service by automatically generating labels through statistical methods and cosine similarity calculations without requiring human annotators. The feedback is labeled by comparing it with sample feedback using vector similarity, enabling the system to annotate its own training data at scale.
Solution Approach 2:
The patent replaces the mechanical human annotation process with an automated computational system. Instead of manual reading and classification by experts, the system uses statistical methods, vector representations, and cosine similarity algorithms to automatically generate labels, eliminating the need for human labor in the annotation process.
3Manufacturing precision
If extensive manual labeling is performed, then training data quality improves, but productivity decreases
Solution Approach 1:
The system performs self-service by automatically generating labels through statistical methods and cosine similarity calculations without requiring human annotators. The feedback is labeled by comparing it with sample feedback using vector similarity, enabling the system to annotate its own training data at scale.
Solution Approach 2:
The system performs preliminary action by pre-computing vector representations of sample feedback and establishing similarity thresholds before actual labeling occurs. This preparation enables rapid automated labeling of new feedback without requiring real-time human intervention, thus improving training data generation speed.
4Productivity
If automated labeling is used, then productivity increases, but measurement precision may decrease
Solution Approach 1:
The patent replaces the mechanical human annotation process with an automated computational system. Instead of manual reading and classification by experts, the system uses statistical methods, vector representations, and cosine similarity algorithms to automatically generate labels, eliminating the need for human labor in the annotation process.
Solution Approach 2:
The system uses parameter changes by adjusting the similarity threshold and controlling the number of iterations in the labeling process. By optimizing these parameters, the system achieves high labeling speed while maintaining sufficient accuracy for training purposes, balancing productivity and precision.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to generate one or more images. In at least one embodiment, one or more neural networks are used to generate information about a computer program based, at least in part, on unannotated user feedback.