DPRM: DeBERTa-based potential relationship multi-headed self-attention joint extraction model
Songjiang Li, Jinming Cao, Jiao Yang, Yunjiangcan He, Peng Wang
TL;DR
This paper introduces DPRM, a new model for extracting entities and relationships in manufacturing data, which outperforms existing methods in fault domain tasks.
Contribution
The novel DPRM model integrates DeBERTa with multi-headed self-attention for improved entity-relationship extraction in manufacturing.
Findings
DPRM outperforms existing models in fault domain entity-relationship extraction.
The model achieves a higher F1 score on fault datasets compared to baselines.
The global entity pairing module simplifies architecture and improves performance.
Abstract
Traditional entity-relationship joint extraction models are typically designed to address generic domain data, which limits their effectiveness when applied to domain-specific applications such as manufacturing. This study presents the DeBERTa-based Potential Relationship Multi-Headed Self-Attention Joint Extraction Model (DPRM), which has been specifically designed to enhance the accuracy and efficiency of entity-relationship extraction in manufacturing knowledge graphs. The model is comprised of three core components: a semantic representation module, a relationship extraction and entity recognition module, and a global entity pairing module. In the semantic representation module, a DeBERTa encoder is employed to train the input sentences, thereby generating word embeddings. The capture of word dependencies is achieved through the utilization of Bi-GRU and Multi-Headed Self-Attention…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsAdvanced Text Analysis Techniques · Functional Brain Connectivity Studies · Topic Modeling