SPO Tuple Recognition via Dependency Parsing and Embedding
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Solution Overview
Problem
Existing methods for recognizing subject-predicate-object (SPO) tuple relationships in natural language sentences using deep learning require large amounts of training data and complex models, leading to high costs and computational expenses, making it challenging to achieve high accuracy efficiently.
Innovation Solution
A method involving parsing dependent syntax from natural language sentences to generate relationship information, embedding this information, and inputting it into a multi-layered bidirectional time series neural network model for training, which includes self-attention and self-decoding processes to recognize SPO tuple relationships with improved accuracy using a simpler deep learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a deep learning model is used to extract SPO tuple relationship information from natural language sentences, then the accuracy of extracting SPO tuple relationship is improved, but the training data requirement increases and the model structure becomes complicated with large parameter sizes, leading to high computational costs
Solution Approach 1:
The patent segments the natural language processing task into distinct modules: dependency parsing module for syntactic analysis, embedding module for converting parsed structures to vectors, and classification module for SPO relationship identification. This segmentation allows each module to be optimized independently and reduces overall model complexity while maintaining accuracy.
Solution Approach 2:
The patent performs dependency parsing and grammatical analysis as preliminary actions before the main classification task. By pre-processing the natural language sentence to extract dependency relationships and grammatical structures, the model reduces the complexity of the main classification task and requires fewer training parameters.
2Measurement precision
If a deep learning model is used to extract SPO tube relationship information from natural language sentences, then the accuracy of extracting SPO tube relationship is improved, but the computational costs increase due to large parameter sizes
Solution Approach 1:
The patent changes the parameter representation by using embedding vectors that capture dependency relationship information in a compressed format. Instead of using large-scale parameters to directly model all possible relationships, the system uses a fixed vocabulary of dependency types with corresponding embedding vectors, significantly reducing the number of parameters while maintaining high accuracy.
3Measurement precision
If existing deep learning methods are used for SPO relationship recognition, then recognition accuracy is improved, but the requirement for large amounts of training data increases
Solution Approach 1:
The patent introduces dependency parsing as an intermediary step between the input natural language sentence and the SPO relationship classification. This intermediary layer extracts structured grammatical information that serves as a bridge, allowing the model to learn from fewer examples by leveraging the structured representation rather than requiring large amounts of raw training data.
Data Source
AI summary
Method of recognizing a SPO tuple relationship based on deep learning includes reading a natural language sentence stored in a first storage in which a natural language sentence and an SPO tuple are configured in pairs and stored, generating dependent syntax relationship information by parsing a dependent syntax from the natural language sentence, reading the SPO tuple paired with the natural language sentence stored in the first storage, generating dependent-syntax-based first SPO tuple relationship information for training a deep learning model based on the SPO tuple, the natural language sentence, and the dependent syntax relationship information, performing embedding on the dependent syntax relation information, recognizing second SPO tuple relationship information by inputting the embedded dependent syntax relationship information to a multi-layered bidirectional time series neural network model, and training the deep learning model by comparing the first and second SPO tuple relationship information.