Text Processing Using Word Relation Tensor for Triplet Extraction
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Solution Overview
Problem
Extracting relational triplets from texts with varying sources and structures is challenging due to complexity in natural language, where entity and relation numbers are not fixed, leading to uncertainty and overlapping relations.
Innovation Solution
A method involving encoding text to generate feature information, identifying entity information, and using a three-dimensional word relation tensor to determine relations between entities, optimizing the tensor to improve triplet extraction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text processing methods are used to extract relational triplets, then the process is simpler, but the accuracy is reduced due to uncertainty and overlapping relations in natural language
Solution Approach 1:
The patent transforms the traditional two-dimensional relation extraction problem into a three-dimensional tensor problem. By introducing a third dimension to represent relation types and their correlations, the model can simultaneously capture multiple relations between entity pairs, thereby improving extraction accuracy while managing complexity through structured dimensionality.
Solution Approach 2:
The patent changes the parameter representation from traditional flat vectors to three-dimensional tensors. This parameter transformation enables the model to encode not only entity features but also relation correlations, allowing for more accurate distinction between overlapping relations through enhanced parameter space.
2Reliability
If the number of entities and relations is increased to capture more information, then the coverage is improved, but the uncertainty and overlapping relations increase
Solution Approach 1:
The patent implements a feedback mechanism where the three-dimensional relation tensor captures correlations between different relations. This feedback loop allows the model to refine relation predictions by considering how different relations interact and overlap, thereby reducing information uncertainty and improving extraction reliability through iterative refinement.
Solution Approach 2:
The patent creates a composite representation by combining entity features, relation features, and their correlations into a unified three-dimensional tensor structure. This composite approach integrates multiple sources of information while maintaining their distinct characteristics, allowing the model to handle increased entity and relation counts without proportionally increasing uncertainty.
3Measurement precision
If a three-dimensional word relation tensor is used to capture relation correlations, then the extraction accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the complex three-dimensional tensor computation into manageable components: entity embedding layer, relation embedding layer, and tensor computation layer. This segmentation allows for optimized computation at each stage, reducing overall computational power requirements while maintaining the ability to capture relation correlations for improved extraction precision.
Data Source
AI summary
A method of processing a text, a training method, a method of generating a knowledge graph, an electronic device, and a non-transitory computer-readable storage medium are provided, which relates to a field of artificial intelligence, particularly to fields such as deep learning, natural language processing, computer vision, and speech processing. The method of processing a text includes: encoding a text to be processed to obtain a feature information; identifying a plurality of entity information from the text, based on the feature information; generating a word relation tensor based on the feature information; and determining a relation between the plurality of entity information by using the word relation tensor, so as to generate a plurality of relational triplets related to the text.