Encoder-Decoder Model for Unstructured Text Structuring
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Solution Overview
Problem
Existing methods for extracting structured information from unstructured text require different extraction strategies for various scenarios, limiting their portability and universality.
Innovation Solution
A method involving an encoder-decoder model is used to input unstructured text, generating an output sequence with attribute elements and values, where the model is trained using marked training text, allowing for flexible attribute setting across scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If different extraction strategies are used for different scenarios, then extraction accuracy is improved, but model complexity and development workload increase
Solution Approach 1:
The patent applies a single encoder-decoder model that can perform information extraction across multiple scenarios and domains. The model is designed with universal components including an encoder for sequence encoding, a decoder for information extraction, and an attention mechanism that adapts to different extraction tasks without requiring separate models for each scenario, thus reducing overall system complexity while maintaining extraction accuracy
Solution Approach 2:
The patent changes the parameters and configuration of the encoder-decoder model to adapt to different extraction scenarios. By adjusting the attention mechanism weights, decoding strategies, and training data parameters rather than creating entirely different models, the system achieves scenario-specific extraction accuracy while avoiding the complexity of multiple specialized models
2Measurement precision
If different extraction strategies are used for different scenarios, then extraction accuracy is improved, but portability and universality decrease
Solution Approach 1:
The encoder-decoder model is designed as a universal framework that can be applied across different information extraction scenarios including named entity recognition, relation extraction, and event extraction. The same model architecture serves multiple functions by processing different types of unstructured text data and producing structured outputs for various applications, thereby improving portability while maintaining accuracy
Solution Approach 2:
The model employs dynamic components including an attention mechanism that adaptively weights different parts of the input sequence based on the extraction task, and a flexible decoding process that can adjust to different output formats. This dynamic adaptability allows the same model to maintain high extraction accuracy across different scenarios without sacrificing portability
3Adaptability or versatility
If multiple extraction strategies are developed for various scenarios, then comprehensive coverage is improved, but developer workload increases
Solution Approach 1:
The encoder-decoder model incorporates self-attention mechanisms and automatic feature learning capabilities that reduce the need for extensive manual feature engineering and strategy customization. The model automatically adapts to different extraction scenarios through its attention mechanism that self-adjusts to focus on relevant information, thereby improving scenario coverage while reducing the developer workload for configuring and maintaining multiple extraction strategies
Data Source
AI summary
A method and an apparatus for structuring data are related to information processing technologies in the field of natural language processing. By acquiring an unstructured text and inputting the unstructured text into an encoder-decoder model, an output sequence is obtained. The encoder-decoder model is trained using a training text marked with the attribute value of each attribute. A structured representation is generated based on the attributes corresponding to the attribute elements included in the output sequence and the attribute values comprised in the attribute elements.