Gazetteer-Neural NER Integration for Ambiguous Entity Recognition
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Solution Overview
Problem
Building artificial intelligence-based chatbots for natural language conversations is challenging due to the subtleties and ambiguities of natural languages, misclassification of named entities, and the need for specialized knowledge in data preparation and model selection, which requires precise training data and iterative development.
Innovation Solution
Integrating a gazetteer with a neural network for named entity recognition to improve accuracy by combining contextual embeddings with gazetteer search results, reducing the need for extensive training data and enabling custom NER systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a neural network is used for named entity recognition, then the system can understand natural language contextually, but the accuracy of named entity recognition deteriorates due to ambiguities and subtleties in natural language
Solution Approach 1:
The patent combines a neural network with a gazetteer system to create a hybrid NER approach. The neural network processes contextual understanding while the gazetteer provides structured entity references, merging the strengths of both systems to improve overall NER accuracy while maintaining natural language adaptability
Solution Approach 2:
The gazetteer acts as an intermediary between the neural network and the final entity recognition output. It mediates by providing structured entity candidates that the neural network can then evaluate in context, resolving ambiguities that the neural network alone cannot handle
2Adaptability or versatility
If specialized developers build chatbots with iterative model development, then the system can be customized for specific fields, but the complexity and time required for data preparation and model selection increases
Solution Approach 1:
The gazetteer is prepared in advance with structured entity data, eliminating the need for extensive iterative model development and data preparation. This preliminary structuring of entity knowledge allows chatbots to be customized for specific fields without requiring complex iterative training processes
Solution Approach 2:
The system uses pre-existing gazetteer structures that can be copied and adapted to different domains. Instead of building custom NER models from scratch for each application, developers can leverage and adapt existing gazetteer frameworks, reducing development complexity while maintaining customization
3Measurement precision
If extensive training data is used for supervised learning, then the model can learn precise patterns and features, but the time and resources required for training increases
Solution Approach 1:
Entity patterns and features are pre-structured in the gazetteer before training begins. This preliminary organization of entity knowledge allows the model to learn from more structured, higher-quality data with fewer examples, reducing the amount of training data and time needed while maintaining or improving pattern recognition accuracy
Data Source
AI summary
Techniques are provided for named entity recognition using a gazetteer incorporated with a neural network. An utterance is received from a user. The utterance is input into a neural network comprising model parameters learned for named entity recognition. The neural network generates a first representation of one or more named entities based on the utterance. A gazetteer is searched based on the input utterance to generate a second representation of one or more named entities identified in the utterance. The first named entity representation is combined with the second named entity representation to generate a combined named entity representation. The combined named entity representation is output for facilitating a response to the user.