NLP Model Training via Knowledge Fusion Vectors
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Solution Overview
Problem
Current natural language processing (NLP) technologies face limitations in training models due to the restricted features present in natural language text, resulting in suboptimal processing effects.
Innovation Solution
A method for training NLP models by obtaining sample text, determining triples within the text (comprising entities and relations), processing the text to generate knowledge fusion vectors, and inputting these vectors into the NLP model for training. The model includes feature extraction layers that perform linear transformations and determine association and weight matrices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If natural language text itself is used for training NLP models, then the training process is simple, but the processing effect is not ideal due to limited features
Solution Approach 1:
The patent merges natural language text with knowledge graph triples (entity-relation-entity structures) to create enriched training data. The text processing module processes both the original text and extracted triples simultaneously, combining linguistic features with structured knowledge features to improve model processing effect while maintaining a unified training framework
Solution Approach 2:
The patent introduces a knowledge graph as an intermediary between raw text and the NLP model. The text processing module extracts triples from text and uses them as intermediate structured representations, which bridge the gap between unstructured natural language and the model's feature requirements, thereby improving processing effectiveness
2Reliability
If knowledge fusion vectors with triples are used for training, then the processing effect is improved, but the training process complexity increases
Solution Approach 1:
The patent segments the training process into distinct modules: a text processing module that handles natural language text and extracts triples, and a knowledge processing module that processes the extracted triples. This segmentation allows each module to specialize in processing its respective data type while working together to create comprehensive knowledge fusion vectors, improving processing effect through modular architecture
Solution Approach 2:
The knowledge fusion vector serves multiple functions: it combines semantic information from text with structured knowledge from triples, provides enriched features for the NLP model, and maintains compatibility with existing model architectures. This multi-functionality allows the system to improve processing effectiveness without requiring fundamental changes to the underlying model structure
Data Source
AI summary
The present disclosure relates to a method for training a natural language processing model, a text processing method and an apparatus, where the method for training a natural language processing model includes: obtaining a sample text; determining a triple in the sample text, where the triple includes two entities in the sample text and a relation between the two entities; processing the sample text based on the triple to obtain a knowledge fusion vector; and inputting the knowledge fusion vector into the natural language processing model for training to obtain a target model. The sample text may be processed based on the triple in the sample text to obtain a knowledge fusion vector. Compared with the sample text, the knowledge fusion vector contains other triple information besides the sample text information itself, so that a computer obtains the true meaning of the sample text more accurately.