LDA-Transformer Model in Chinese Poetry Authorship Attribution

Zhou, Ai; Zhang, Yijia; Wu, Hao; Lu, Mingyu

doi:10.1007/978-3-030-88189-4_5

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Logistic Regression (LR) is the most commonly used classifier in previous pre-trained LLMs-based studies. [10,12,39]. Therefore, we introduce the following classifiers, which combine LR with other neural networks, into MSDD-(IA) 3 :…”

Section: Approachmentioning

confidence: 99%

Parameter-Efficient Multi-classification Software Defect Detection Method Based on Pre-trained LLMs

Wang,

Lu,

Yang

et al. 2024

Int J Comput Intell Syst

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Software Defect Detection (SDD) has always been critical to the development life cycle. A stable defect detection system can not only alleviate the workload of software testers but also enhance the overall efficiency of software development. Researchers have recently proposed various artificial intelligence-based SDD methods and achieved significant advancements. However, these methods still exhibit limitations in terms of reliability and usability. Therefore, we introduce MSDD-(IA)3, a novel framework leveraging the pre-trained CodeT5+ and (IA)3 for parameter-efficient multi-classification SDD. This framework constructs a detection model based on pre-trained CodeT5+ to generate code representations while capturing defect-prone features. Considering the high overhead of pre-trained LLMs, we injects (IA)3 vectors into specific layers, where only these injected parameters are updated to reduce the training cost. Furthermore, leveraging the properties of the pre-trained CodeT5+, we design a novel feature sequence that enriches the input data through the combination of source code with Natural Language (NL)-based expert metrics. Our experimental results on 64K real-world Python snippets show that MSDD-(IA)3 demonstrates superior performance compared to state-of-the-art SDD methods, including PM2-CNN, in terms of F1-weighted, Recall-weighted, Precision-weighted, and Matthews Correlation Coefficient. Notably, the training parameters of MSDD-(IA)3 are only 0.04% of those of the original CodeT5+. Our experimental data and code can be available at (https://gitee.com/wxyzjp123/msdd-ia3/).

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Section: Approachmentioning

confidence: 99%

Parameter-Efficient Multi-classification Software Defect Detection Method Based on Pre-trained LLMs

Wang,

Lu,

Yang

et al. 2024

Int J Comput Intell Syst

View full text Add to dashboard Cite

show abstract

Generating Authorship Embeddings with Transformers

Huertas‐Tato¹,

Martı́n²,

Huertas-Garcia³

et al. 2022

2022 International Joint Conference on Neural Networks (IJCNN)

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Exploring Thematic Diversity in Classical Chinese Poetry: A Novel Dataset and a BERT-enhanced Ensemble Learning Approach

Hou,

Zhang

2024

J. Comput. Cult. Herit.

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Classical Chinese poetry, as an essential aspect of cultural heritage, exhibits rich theme diversity often overlooked in natural language processing research. To address this gap, we aim to explore the classification of thematic categories within this literary domain. We curate a dataset of 2,918 annotated poems spanning seven common themes and propose a BERT-based ensemble learning approach for effective classification. Although this method integrates existing models, it achieves an accuracy and F1 score of over 72% in the 7-class task, surpassing established baselines, and providing a baseline for future research. The experimental findings reveal the effectiveness of ensemble strategies in improving individual base model performance and highlight the potential of the MLP-based ensemble technique. The study contributes to a deeper understanding of thematic categories and textual features in classical Chinese poetry, and offers an automated classification system for classical Chinese poems.

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LDA-Transformer Model in Chinese Poetry Authorship Attribution

Cited by 3 publications

References 10 publications

Parameter-Efficient Multi-classification Software Defect Detection Method Based on Pre-trained LLMs

Parameter-Efficient Multi-classification Software Defect Detection Method Based on Pre-trained LLMs

Generating Authorship Embeddings with Transformers

Exploring Thematic Diversity in Classical Chinese Poetry: A Novel Dataset and a BERT-enhanced Ensemble Learning Approach

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