Triplet Information Extraction via Data Augmentation and Semi-Supervised Learning
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Solution Overview
Problem
Current methods for extracting triplet information from corpora are costly due to the high labeling requirements and often result in semantic loss, as they fail to effectively interact feature information between entity recognition and relationship classification subtasks.
Innovation Solution
A computer-implemented method for data augmentation that involves acquiring second sample corpora and triplet information through data augmentation of labeled corpora, and third triplet information through semi-supervised learning on unlabeled corpora, to generate training corpora for a triplet information extraction network, utilizing techniques like entity replacement, synonym replacement, and back translation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a large number of corpora are manually labeled for triplet information extraction, then the quality and quantity of training data improve, but the labeling cost and time consumption increase significantly
Solution Approach 1:
The system performs self-service through automated data augmentation and semi-supervised learning. The model generates pseudo-labeled data from unlabeled corpora, and the training process automatically iterates to improve performance without requiring continuous manual labeling intervention.
Solution Approach 2:
The method performs preliminary data augmentation on a small set of labeled corpora before main training. By pre-processing the limited labeled data through multiple augmentation techniques (entity replacement, synonym replacement, back translation), the system prepares enhanced training samples in advance to maximize the utility of scarce labeled resources.
2Quantity of substance
If simple vocabulary augmentation is applied to expand training data, then the data quantity increases, but semantic loss occurs and the augmentation effect remains limited
Solution Approach 1:
The system changes multiple parameters simultaneously during data augmentation: entity types are replaced with semantically equivalent entities, synonyms are substituted while preserving meaning, and linguistic structures are transformed through back translation. These coordinated parameter changes expand data quantity while maintaining semantic integrity.
Solution Approach 2:
The method combines multiple data augmentation techniques (entity replacement, synonym replacement, back translation) into a composite augmentation pipeline. Each technique contributes different aspects of semantic preservation, and their combination creates a more robust augmentation approach than any single method alone.
3Device complexity
If entity recognition and relationship classification are treated as independent subtasks, then the model structure simplifies, but the correlation between the two subtasks is ignored and feature information interaction is reduced
Solution Approach 1:
The system merges entity recognition and relationship classification into a unified joint training framework. The model processes both subtasks simultaneously, allowing feature interactions between entity identification and relationship prediction. The loss function combines both tasks, enabling gradient updates that consider both objectives together.
Solution Approach 2:
The approach adds a joint training dimension to the traditional sequential processing. Instead of treating entity recognition and relationship classification as separate sequential steps, the system introduces a multi-task learning dimension where both tasks are optimized together, enabling cross-task feature sharing and interaction.
Data Source
AI summary
A computer-implemented method for sample augmentation includes: acquiring a second sample corpus and second triplet information of the second sample corpus by performing data augmentation on a first sample corpus labeled with first triplet information; acquiring third triplet information of a third sample corpus by performing semi-supervised learning on the third sample corpus that is not labeled with triplet information; and generating a set of training corpora for a triplet information extraction network based on the first sample corpus and the first triplet information, the second sample corpus and the second triplet information, and the third sample corpus and the third triplet information.