Unified Information Extraction Model for Text Triple Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing information extraction methods in natural language processing are inefficient and prone to errors due to the separation of entity recognition and relationship extraction, leading to lower accuracy in semantic comprehension.
Innovation Solution
A method and apparatus for information extraction using an end-to-end information extraction model with a binary classification sub-model for subject extraction and a multi-label classification sub-model for predicate and object extraction, jointly trained with pre-trained language and neural network models, improving efficiency and accuracy by directly extracting triple information from text data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional separate entity recognition and relationship extraction methods are used, then the process is simpler to implement, but the extraction accuracy decreases
Solution Approach 1:
The patent combines entity recognition and relationship extraction into a single unified information extraction model. The model simultaneously performs both tasks by integrating the entity recognition module and relationship extraction module, allowing joint training and shared representations, which improves extraction accuracy while managing model complexity through unified architecture design.
Solution Approach 2:
The unified model is segmented into distinct functional modules: an embedding layer for text representation, an entity recognition module for identifying entities, a relationship extraction module for extracting relationships, and a triple output module. This modular segmentation allows the complex model to be managed and trained efficiently while maintaining high extraction accuracy through specialized sub-components.
2Productivity
If traditional multi-step information extraction methods are used, then the model structure is simpler, but the processing time increases
Solution Approach 1:
The model performs entity recognition and relationship extraction in a single unified processing pass rather than sequentially. The preliminary embedding layer processes the entire input text once, and both entity recognition and relationship extraction operate on these shared representations simultaneously, eliminating the need for multiple separate processing steps and reducing overall processing time.
Solution Approach 2:
By merging entity recognition and relationship extraction into a single unified model that processes input text in one pass, the patent eliminates the sequential processing overhead of traditional multi-step methods. The shared representations and joint training approach enable simultaneous extraction of both entities and relationships, significantly improving extraction efficiency.
3Measurement precision
If separate entity recognition and relationship extraction models are used, then each model can be optimized independently, but the overall extraction accuracy decreases
Solution Approach 1:
The patent merges entity recognition and relationship extraction into a single unified model with shared representations. This allows the models to learn joint features and dependencies between entities and relationships, improving extraction accuracy. The unified model is trained end-to-end with a single loss function that combines entity recognition loss and relationship extraction loss, simplifying the training process despite the increased model capabilities.
Solution Approach 2:
The unified information extraction model serves multiple functions simultaneously: it performs entity recognition, relationship extraction, and triple generation in one system. The shared embedding layer and common feature representations enable the model to leverage information from both tasks, improving overall accuracy while maintaining ease of training through a single unified training pipeline.
Data Source
AI summary
The present disclosure provides a method and an apparatus for information extraction, an electronic device, and a storage medium. The method for information extraction includes: first obtaining text data, and then inputting the text data into an information extraction model obtained through pre-training to obtain triple information contained in the text data, wherein the triple information includes a subject, a predicate and an object in the text data. The information extraction model includes a binary classification sub-model and a multi-label classification sub-model, wherein the binary classification sub-model is configured to extract the subject in the text data, and the multi-label classification sub-model is configured to extract the predicate and the object corresponding to the subject in the text data according to the subject and the text data.