AI Server Keyword Ranking for NLP Model Training
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Solution Overview
Problem
Existing natural language processing models struggle to accurately recognize and understand user-specific keywords and phrases, leading to inefficiencies in training data generation and model performance.
Innovation Solution
An artificial intelligence server that collects user dictionaries, calculates keyword rankings, and generates a cloud user dictionary to train a natural language processing model, improving its ability to recognize and understand user-specific terminology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a general natural language processing model is trained without user-specific keywords, then the model structure remains simple and training data generation is efficient, but the model cannot accurately recognize and understand user-specific terminology
Solution Approach 1:
The patent segments the natural language processing model into two distinct parts: a general NLP model for common language processing and a user-specific keyword model for personalized terminology. This segmentation allows the system to maintain simplicity for general processing while adding specialized capability only where needed, resolving the contradiction between accuracy and complexity.
Solution Approach 2:
The system performs preliminary action by pre-collecting user-specific keywords and pre-training a specialized keyword model before integrating it with the general NLP model. This advance preparation ensures that when user-specific recognition is needed, the model is already equipped with the necessary specialized knowledge, improving accuracy without requiring complex real-time adaptations.
2Reliability
If user-specific keywords are collected and integrated into training data, then the model's understanding of unique phrases improves, but the time and resources required for training data generation increase
Solution Approach 1:
The patent extracts user-specific keywords from user profiles and isolates them into a separate training dataset distinct from general language data. This extraction allows the system to train a specialized keyword model using only the necessary user-specific information, rather than processing entire user profiles or general language corpora, significantly reducing training time while maintaining reliability.
Solution Approach 2:
The system applies partial action by selectively training only on the most relevant user-specific keywords rather than all possible user data. By focusing computation on the essential personalized elements, the system achieves improved model performance without the excessive time cost of processing complete user histories or general language datasets.
3Adaptability or versatility
If a comprehensive user dictionary is created with all user keywords, then the model can recognize all user-specific terms, but the complexity of managing and processing the dictionary increases
Solution Approach 1:
The patent segments the comprehensive user dictionary into structured categories and hierarchical levels, organizing keywords by relevance and frequency. This segmentation transforms an unmanageable flat list into an organized structure that is easier to process and integrate with the NLP model, maintaining high keyword coverage while reducing management complexity.
Solution Approach 2:
The system adds dimensional organization to the user dictionary by introducing hierarchical levels and categorical dimensions. Instead of a single-dimensional flat list, the dictionary is structured across multiple dimensions (frequency, relevance, category), enabling more efficient processing and integration while preserving comprehensive keyword coverage.
Data Source
AI summary
Disclosed herein is an artificial intelligence server which receives a user dictionary, calculates rankings of a plurality of keywords included in the user dictionary, generates a cloud user dictionary according to the rankings of the plurality of keywords, and trains a natural language processing model.