Predictive Personal NLP Models for User-Specific Term Recognition
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Solution Overview
Problem
Natural language processing systems face limitations in accurately recognizing terms and intents used by individual users, as general models struggle to discriminate between user-specific and general population usage, while personalized models provide limited coverage due to their smaller size.
Innovation Solution
The generation of predictive personal models based on user-specific data, demographics, and historical interactions, which include predicted terms and intents likely to be used by the user, allowing for improved accuracy in processing both familiar and unfamiliar inputs by weighting personal, predictive, and general models during language processing tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If general models are used for natural language processing, then coverage of general population terms and intents is improved, but accuracy in recognizing user-specific terms and intents deteriorates
Solution Approach 1:
The language model is segmented into multiple components: a general model for population-wide terms, a personalized model for user-specific terms, and a predictive model that combines both approaches. This segmentation allows each component to specialize in different aspects, with the personalized model handling user-specific terminology and the general model providing broad coverage, while the predictive model integrates them through weighted combinations based on user profile characteristics.
Solution Approach 2:
The system applies local quality by creating a personalized model tailored to each user's specific terminology, demographics, and usage patterns. Instead of using a uniform general model for all users, the system adapts the model parameters and vocabulary based on individual user characteristics, such as age, gender, location, and historical interactions, thereby improving accuracy for each user's specific needs while maintaining general coverage capabilities.
2Measurement precision
If personalized models are used for natural language processing, then accuracy in recognizing user-specific terms and intents is improved, but coverage of unfamiliar terms and intents deteriorates
Solution Approach 1:
The system merges the general model and personalized model into a unified predictive model that combines their strengths. The predictive model integrates the broad vocabulary and knowledge from the general model with the user-specific terminology and patterns from the personalized model, creating a comprehensive model that handles both familiar and unfamiliar terms accurately through weighted combinations based on user profile and context.
Solution Approach 2:
The predictive model serves multiple functions simultaneously: it provides user-specific term recognition like a personalized model, maintains general population coverage like a general model, and predicts likely terms based on user profiles. This multi-functionality allows a single model to replace both specialized models while achieving superior performance across all tasks.
3Adaptability or versatility
If larger models are used to increase coverage, then range of terms and intents is improved, but model size and computational complexity increase
Solution Approach 1:
The system employs dynamic model selection and weighting based on user profile characteristics, input context, and task requirements. Instead of using a single large model, the system dynamically adjusts which model components to activate and their relative weights, allowing the system to adapt the model size and complexity to the specific task at hand, thereby reducing computational overhead while maintaining comprehensive coverage when needed.
Data Source
AI summary
Features are disclosed for generating predictive personal natural language processing models based on user-specific profile information. The predictive personal models can provide broader coverage of the various terms, named entities, and/or intents of an utterance by the user than a personal model, while providing better accuracy than a general model. Profile information may be obtained from various data sources. Predictions regarding the content or subject of future user utterances may be made from the profile information. Predictive personal models may be generated based on the predictions. Future user utterances may be processed using the predictive personal models.