News Snippet Classification via BILSTM and PLM Ensemble
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Solution Overview
Problem
Manual categorization of business news snippets is labor-intensive and ineffective in organizing and disseminating news, as traditional classification techniques fail to group articles based on major events, leading to overwhelming demands on consumers and organizations.
Innovation Solution
A method and system using an ensemble of machine learning models, specifically a bidirectional long short-term memory (BILSTM) based text classification network and a pre-trained language model (PLM) for natural language inference (NLI), to automatically classify news snippets into categories by learning text representations and utilizing premise-hypothesis pairs for fine-tuning, reducing manual work and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual categorization is used to classify news snippets, then categorization accuracy can be maintained through human judgment, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system segments the categorization task into multiple independent machine learning models, each trained on different aspects of news text (e.g., headline, body, metadata). This segmentation allows parallel processing of different features while maintaining comprehensive categorization accuracy, reducing the time burden compared to sequential manual review.
Solution Approach 2:
The patent replaces the mechanical human categorization system with an automated machine learning-based system. Multiple ML models process news snippets automatically, substituting human labor with computational algorithms that can handle large volumes of data without time constraints, while maintaining or improving categorization accuracy through ensemble methods.
2Ease of manufacture
If traditional classification techniques are used to organize news articles, then implementation simplicity is maintained, but the ability to group articles based on major events is insufficient
Solution Approach 1:
The system employs a composite approach by integrating multiple machine learning models with different strengths (e.g., transformer models, LSTM, traditional classifiers) to create an ensemble system. This composite structure combines the advantages of various algorithms, improving categorization effectiveness for event-based grouping while maintaining implementation feasibility through modular architecture.
Solution Approach 2:
The machine learning system is designed to perform multiple functions: it can categorize news by topic, identify major events, extract entities, and prioritize articles. This multi-functionality allows a single system to replace multiple traditional classification tools, improving reliability without significantly increasing implementation complexity.
3Measurement precision
If multiple machine learning models are used to classify news snippets, then categorization accuracy improves through ensemble methods, but system complexity increases
Solution Approach 1:
The ensemble system is segmented into specialized models that each handle specific categorization aspects. This segmentation allows for targeted model selection and reduces the computational burden of running all models for every input, managing complexity while maintaining accuracy through focused specialization of each model component.
4Ease of operation
If manual sorting and chronological arrangement are performed on news articles, then information organization is achieved, but productivity and scalability are limited
Solution Approach 1:
The system replaces manual sorting and chronological arrangement operations with automated machine learning pipelines that can process and organize news articles simultaneously. The ML models perform categorization, event detection, and temporal ordering in an integrated automated workflow, dramatically increasing productivity while maintaining ease of information retrieval for users.
Data Source
AI summary
This disclosure relates generally to method and system to classify news snippets into categories using an ensemble of machine learning models. The ensemble is between a bidirectional long short memory (BILSTM) based text classification network and a pretrained language model (PLM) based natural language inference (NLI) which is robust and accurate for such categorization. The method trains a first machine learning model using a training dataset to learn text representations. Further, the training dataset is used to finetune a second machine learning model to classify at least one unlabeled news snippet of unknown category based on a premise-hypothesis pair. Further, an ensemble of machine learning models is generated by using the first machine learning model and the second machine learning model to classify a set of test news snippets received as input request to corresponding category.