Event Profiling via Neural Network Ensemble Disambiguation
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Solution Overview
Problem
Conventional automated event profiling methods face challenges in handling nuances of natural language, ensuring information verifiability, reliability of sources, and managing factual variations or contradictions, particularly in multilingual contexts.
Innovation Solution
A processor-implemented method and system for event profiling that aggregates multilingual articles, utilizes language-specific Natural Language Processors (NLPs) to identify relevant linguistic features, classifies information using language-specific classifiers, disambiguates class membership through voting, and updates an event ontology using semi-supervised techniques for incremental updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If keyword based approaches are used for automated event profiling, then the processing speed is improved, but the accuracy of contextual interpretation deteriorates
Solution Approach 1:
The patent replaces traditional keyword-based mechanical text processing with neural network-based natural language processing systems. The neural networks automatically learn and extract meaningful features from text, enabling both high processing speed and accurate contextual interpretation without relying on predefined keywords.
Solution Approach 2:
The system dynamically adjusts processing parameters by using multiple neural networks with different architectures and configurations. By changing the parameters of the neural networks (such as layer structures, activation functions, and training data), the system optimizes both processing efficiency and interpretation accuracy for different event types.
2Measurement precision
If manual event profiling is performed, then the accuracy of information extraction is improved, but the time consumption increases
Solution Approach 1:
The patent replaces manual expert analysis with automated neural network systems that can process and extract information from multiple news sources simultaneously. The neural networks are trained to recognize patterns and extract event details with accuracy comparable to or exceeding manual profiling, while operating at much higher speeds.
Solution Approach 2:
The system employs self-learning neural networks that automatically improve their extraction accuracy over time by learning from new data without requiring continuous manual intervention. The networks autonomously adjust their parameters and refine their extraction capabilities, maintaining high accuracy while minimizing time consumption.
3Adaptability or versatility
If automated event profiling handles multilingual sources, then the coverage of information sources is improved, but the complexity of language processing increases
Solution Approach 1:
The patent implements universal neural network architectures that can process multiple languages using the same underlying structure. The networks are trained on multilingual data and can automatically adapt to different languages, providing broad information source coverage without requiring separate processing systems for each language.
Solution Approach 2:
The system uses language translation and normalization as intermediary steps before feeding text into the neural networks. This intermediary processing layer converts diverse linguistic inputs into a unified representation that the neural networks can efficiently process, reducing overall system complexity while maintaining multilingual capability.
4Reliability
If multiple classifying agents are used for disambiguation, then the reliability of information is improved, but the computational complexity increases
Solution Approach 1:
The patent combines multiple classifying agents into a unified neural network ensemble that performs disambiguation collectively. Rather than running separate independent classifiers, the system merges their functions into coordinated neural network structures that share computational resources and produce consistent results, reducing overall computational complexity while maintaining high reliability.
Solution Approach 2:
The system implements feedback mechanisms where the outputs of multiple classifying agents are continuously evaluated and adjusted. The neural networks use feedback from their own predictions and from external validation to refine their classification decisions, improving reliability through iterative optimization without requiring proportional increases in computational complexity.
Data Source
AI summary
System and method for method and system for event profiling is described that processes large volume of data gathered from a plurality of digital sources to automatically profile and continuously update an event. The system utilizes, an ensemble of probabilistic classifiers for automated extraction of finer details of the event, which use linguistic features for profiling information about the event, wherein the information is spread across various data sources. Further, disambiguation is performed to augment the accuracy of the event profiling. The system enables semantically linking of related events curated in the knowledge base and thereby performs semantic search over it. The system takes user-feedback and improves upon the information extraction process through reinforcement learning.