Context-Specific Language Model for Accurate Command Interpretation
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Solution Overview
Problem
Existing natural language processing systems struggle to accurately interpret context-dependent natural language inputs, leading to inconsistencies in understanding and executing user commands, as the same input can have different meanings based on context, and similar inputs can be expressed using different words or phrases.
Innovation Solution
A context-specific language model is generated by calculating relatedness values between words based on their frequencies within specific contexts, creating a matrix that represents relationships between words, allowing the system to understand and modify inputs to align with intended actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a general language model is used to interpret natural language inputs, then the system can handle a broad range of inputs, but the accuracy of interpretation decreases when context-specific meanings are required
Solution Approach 1:
The patent segments the language model into multiple context-specific models, each trained on particular domains or scenarios. This allows the system to select the appropriate model based on the input context, thereby improving interpretation accuracy for context-dependent inputs while maintaining versatility through the collection of specialized models.
Solution Approach 2:
The system dynamically selects and switches between different language models based on the detected context of the input. This dynamic adaptation enables the system to maintain high interpretation accuracy across various contexts without requiring a single static model to handle all scenarios equally well.
2Measurement precision
If context-specific language models are generated for different domains, then the interpretation accuracy improves, but the system complexity increases
Solution Approach 1:
The system performs preliminary context analysis to determine which language model is appropriate for the given input before proceeding with interpretation. This preliminary action simplifies the overall system architecture by enabling selective model usage rather than requiring all models to be actively maintained and processed simultaneously.
Solution Approach 2:
The patent introduces a context detection mechanism that acts as an intermediary between the input and the language models. This mediator analyzes the input context and routes it to the appropriate model, thereby managing system complexity by providing a centralized control layer that coordinates model selection.
3Productivity
If the system uses traditional natural language processing methods, then the processing speed is maintained, but the ability to understand context-dependent meanings and synonyms is limited
Solution Approach 1:
The patent changes the parameters of the language processing system by using pre-trained language models with contextual understanding capabilities instead of traditional NLP methods. This parameter change enables the system to understand context-dependent meanings and synonyms while maintaining acceptable processing speeds through efficient model architecture and optimization techniques.
Data Source
AI summary
A method, an electronic device, and computer readable medium is provided. The method includes identifying a frequency of each word that is present within a set of words. The method also includes deriving relatedness values for pairs of words. Each pair of words includes a first word and a second word in the set of words. Each relatedness value corresponds to a respective one of the pairs of words. Each relatedness value is based on the identified frequencies that the first word and the second word of the respective pair of words are present within the set of words. The method further includes generating a matrix representing the relatedness values. The method additionally includes generating a language model that represents relationships between the set of words included in the matrix.