Personalized Named Entity Recognition via Model Interpolation
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Solution Overview
Problem
Natural language processing systems face challenges in accurately recognizing named entities, especially for specific users or groups, as general models may not cover unique terms or phrases used by individuals or groups, leading to reduced accuracy and increased computational resources for customized models.
Innovation Solution
The development of personalized and group-specific named entity recognition models, which are generated using user data and interpolated with general models to provide both user-specific accuracy and broad coverage, reducing computational requirements and improving processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a general named entity recognition model is used, then broad coverage of common terms is achieved, but accuracy for user-specific or group-specific named entities deteriorates
Solution Approach 1:
The system segments the named entity recognition task into two distinct models: a general model for common terms and a user-specific model for unique terms. This segmentation allows each model to be optimized for its specific purpose, with the general model providing broad coverage and the user-specific model providing high accuracy for individualized named entities.
Solution Approach 2:
The system merges the general model and user-specific model into a unified named entity recognition system. The user-specific model is trained by modifying the general model's parameters using user data, combining the strengths of both broad coverage and user-specific accuracy in a single integrated system.
2Measurement precision
If a customized named entity recognition model is trained for each user, then user-specific accuracy is improved, but computational resources and training time increase
Solution Approach 1:
Instead of training a completely new model for each user from scratch, the system uses partial action by modifying the general model's parameters with user-specific data. This approach achieves user-specific accuracy without the full computational cost of training entirely new models, leveraging the pre-trained general model as a foundation.
Solution Approach 2:
The system changes the parameters of the general model by training it on user-specific data to create a user-specific model. This parameter modification approach allows the model to adapt to individual users' naming patterns and preferences while maintaining the computational efficiency of using a single base model architecture.
3Measurement precision
If extensive computational resources are allocated for model training, then model accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-training a general named entity recognition model that captures common naming patterns across all users. This pre-trained model serves as an efficient baseline that can be quickly adapted to individual users without requiring extensive computational resources or time for each user-specific model.
Solution Approach 2:
The general model serves a universal function by providing the foundation for all user-specific models. It captures common linguistic patterns and named entity types that apply across the entire user base, enabling rapid adaptation to individual users while maintaining high accuracy and minimizing processing time.
Data Source
AI summary
Features are disclosed for generating and using personalized named entity recognition models. A personalized model can be trained for a particular user, and then interpolated with a general model for use in named entity recognition. In some embodiments, a model may be trained for a group of users, where the users share some similarity relevant to language processing. In some embodiments, various base models may be trained so as to provide better accuracy for certain types of language input than a general model. Users may be associated with any number of base models, and the associated based models may then be interpolated for use in named entity recognition on input from the corresponding user.