Chinese Named Entity Recognition (CNER) is extensively researched in general domains, while, in practical engineering applications, it receives more and more attention in specialized fields. However, CNER’s performance in domain-specific areas, such as in petroleum refining and entertainment, remains moderate due to a lack of annotated data. In this paper, we mainly focus on two improvements related to the problem of scarce annotated data. Firstly, we propose a novel data augmentation method named Knowledge Graph Text Alignment with BART (KGTA-BART), which, for the first time, introduces a knowledge graph extracted from structured and semi-structured data, aligns its graphic information with the semantic information of annotated text, and thus generates high-quality text from the knowledge graph using BART model. Expanding the dataset can help the model learn more entity features and improve its effectiveness when annotated data is scarce. Additionally, we develop the CNER model Virtual Adversary with BART (VA-BART), which utilizes BART as an encoder and applies the virtual adversary to CNER. This improves the capture of contextual information in the text when annotation data is scarce and enhances the model’s generalization ability. Experimental results demonstrate that VA-BART method based on KGTA-BART achieves significant improvements over the baselines when applied to domain-specific dataset in Chinese language.

Sparse direct solvers play a vital role in large-scale high performance computing in science and engineering. Existing distributed sparse direct methods employ multifrontal/supernodal patterns to aggregate columns of nearly identical forms and to exploit dense basic linear algebra subprograms (BLAS) for computation. However, such a data layout may bring more unevenness when the structure of the input matrix is not ideal, and using dense BLAS may waste many floating-point operations on zero fill-ins.