ML Chunker Model for SVO Extraction from Unstructured Text
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Solution Overview
Problem
Current information extraction techniques for extracting subject-verb-object (SVO) triples from unstructured text are inefficient due to reliance on rule-based systems, manual rule development, and overhead from part-of-speech (PoS) tagging, chunking, and parsing processes, which limit extraction efficiency and require significant manual effort.
Innovation Solution
A method and system using a machine learning chunker model trained on SVO annotated data to directly identify and extract SVO chunked text from unstructured text, eliminating the need for rule-based systems and post-chunking processing by employing OpenNLP utilities for PoS tagging and chunking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule-based systems are used for information extraction, then extraction can be performed without labeled data, but the system requires manual rule development and updates, reducing productivity
Solution Approach 1:
The patent replaces manual rule-based systems with a machine learning model that automatically learns extraction patterns from data. The ML model substitutes the mechanical process of manual rule creation and updating, enabling the system to adapt to new extraction tasks without manual intervention while maintaining high extraction efficiency.
Solution Approach 2:
The machine learning model performs self-training and self-updating by learning from labeled data and automatically improving its extraction capabilities. This self-service mechanism eliminates the need for manual rule development and updates, allowing the system to maintain high productivity while adapting to new extraction requirements.
2Measurement precision
If multiple processing steps (PoS tagging, chunking, parsing) are employed, then extraction precision can be improved, but the overhead increases and slows down the extraction process
Solution Approach 1:
The patent combines multiple processing steps (PoS tagging, chunking, and SVO extraction) into a single integrated machine learning model. This merging eliminates the sequential overhead of separate processing steps while maintaining the precision benefits of each individual step, as the model learns to perform all functions simultaneously during training.
Solution Approach 2:
The machine learning model performs preliminary learning of extraction patterns during the training phase on labeled data. This preliminary action allows the model to internalize complex extraction logic beforehand, enabling it to perform accurate extractions during inference without requiring multiple sequential processing steps, thus reducing processing time while maintaining precision.
3Ease of operation
If manual rule development is used, then extraction can be performed on unstructured data, but significant manual effort is required, reducing productivity
Solution Approach 1:
The patent replaces manual rule development with an automated machine learning approach. The ML model automatically learns extraction rules and patterns from labeled training data, substituting the manual mechanical process of rule creation. This enables the system to maintain ease of operation on unstructured data while eliminating significant manual effort, thereby improving productivity.
4Adaptability or versatility
If rule-based engines are employed, then extraction can proceed without labeled data, but the rules must be manually updated, increasing device complexity
Solution Approach 1:
The machine learning model performs self-updating by automatically learning new extraction patterns from labeled data. This self-service capability allows the system to maintain adaptability to new extraction tasks without requiring manual rule updates, thereby reducing the complexity of rule management while preserving extraction flexibility.
Solution Approach 2:
The patent transitions from fixed manual rules to dynamic learned parameters in the machine learning model. The model's parameters are automatically adjusted during training based on labeled data, enabling the system to adapt to new extraction requirements without manual intervention. This parameter-based approach reduces management complexity compared to manual rule-based systems while maintaining versatility.
Data Source
AI summary
This disclosure relates generally to natural language processing, and more particularly to a system and method for extracting subject-verb-object (SVO) chunked text from an unstructured text. In one embodiment, a method is provided for extracting SVO chunked text from an unstructured text. The method comprises identifying a plurality of part of speech (PoS) tokens in the unstructured text, and determining a plurality of SVO chunked text directly from the plurality of PoS tokens using a machine learning chunker model. The machine learning chunker model is trained on a subject-verb-object (SVO) annotated training data.