Relation Extraction Method Using Template-Based Seed Evaluation
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Solution Overview
Problem
Current natural language recognition technologies face high labor and time costs due to the manual annotation of text data required for deep learning model training in relation extraction.
Innovation Solution
A method for relation extraction that automatically generates and refines templates by distinguishing between correct and error seeds, using evaluation indices to select and iteratively improve the accuracy of entity pairs, ultimately reducing manual annotation efforts and enhancing the efficiency of deep learning model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to provide training data for deep learning models, then the quality and accuracy of relation extraction can be improved, but the labor cost and time consumption increase significantly
Solution Approach 1:
The system performs self-annotation by automatically generating candidate entity pairs and relations through template matching and evaluation, eliminating the need for extensive manual annotation. The deep learning model trains on automatically generated data, achieving high accuracy without human labor for each annotation task.
Solution Approach 2:
The method pre-generates candidate entity pairs and relations using templates before model training, preparing high-quality training data in advance. This preliminary automated annotation process creates a robust dataset that eliminates the need for time-consuming manual annotation during model development.
2Reliability
If manual annotation is performed for each sentence in the text, then the training data quality improves, but the labor cost increases
Solution Approach 1:
The system uses template copying and filling to generate candidate entity pairs automatically. Templates define relation patterns that are copied and instantiated with specific entities from the text, producing high-quality training data without manual annotation of each sentence.
Solution Approach 2:
The annotation process is automated through self-service mechanisms where the system generates its own training data using template matching, entity recognition, and evaluation functions, eliminating the need for human annotators to process each sentence individually.
3Measurement precision
If templates are selected based on evaluation indices to generate new seeds, then the precision of relation extraction improves, but the computational complexity increases
Solution Approach 1:
The system implements feedback through evaluation indices that assess template quality based on generated seeds. Templates are iteratively refined and selected based on their performance in generating high-quality entity pairs, with the evaluation feedback guiding subsequent template improvements and selections.
Solution Approach 2:
The template selection process is dynamic and adaptive, with templates being evaluated, refined, and reselected based on their performance metrics. The system dynamically adjusts template parameters and selections to optimize relation extraction precision while managing computational complexity through iterative improvement.
Data Source
AI summary
A method of relation extraction, a non-transient computer storage medium, and a computing device are provided. The method of relation extraction includes: traversing each sentence in a text to be annotated to generate a first template and selecting the first template; traversing each sentence in the text to be annotated, based on the selected first template, to match at least one new seed; evaluating the at least one new seed having been matched; repeating the above steps until a selected condition is met, outputting the matched correct seed and a classification relationship between a first entity and a second entity in the matched correct seed; and training a deep learning model to acquiring a relationship extraction model by using at least some of the sentences in the text having been annotated.