NLP Model Training With Entity-Relation Knowledge Fusion
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Solution Overview
Problem
Natural language processing models trained using traditional text samples exhibit limited processing effectiveness due to the lack of comprehensive features, particularly in capturing entity and relation information.
Innovation Solution
A method involving the extraction of triples from sample text, including entities and their relations, to generate knowledge fusion vectors, which are used to pretrain a natural language processing model through various tasks, enhancing the model's understanding by incorporating additional knowledge information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If natural language text is used as training samples, 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 to create enriched training samples. The text data is combined with structured knowledge (entities, relations, and attributes from knowledge graphs) to form composite training samples that contain both linguistic patterns and factual knowledge, thereby improving feature completeness and processing effect while maintaining training feasibility
Solution Approach 2:
The training samples are constructed as composite data structures combining unstructured text with structured knowledge graph elements. This composite approach integrates multiple data types (text sequences, entity representations, relation triples) into unified training samples, enabling the model to learn both language patterns and knowledge representations simultaneously
2Manufacturing precision
If knowledge fusion vectors are generated from triples, then the processing effect is improved, but the device complexity increases
Solution Approach 1:
The patent performs preliminary construction of knowledge graphs and extraction of triples before the main training process. By pre-processing the knowledge data and organizing it into structured triples (subject, predicate, object), the system prepares enriched training samples in advance, which simplifies the actual model training while achieving improved processing effects through the pre-integrated knowledge features
Solution Approach 2:
The patent introduces knowledge fusion vectors as an intermediary representation that bridges natural language text and structured knowledge. These vectors serve as a mediating data structure that encodes both textual and knowledge graph information, enabling the model to process complex information without requiring direct integration of disparate data formats, thus managing system complexity
Data Source
AI summary
The present disclosure relates to a method for training a natural language processing model, including: obtaining a sample text of natural language; determining one or more triples in the sample text, wherein each of the triples comprises two entities in the sample text and a relation between the two entities; processing the sample text based on the triples to obtain one or more knowledge fusion vectors; and training a natural language processing model by inputting the knowledge fusion vectors into the natural language processing model to obtain a target model.