Analyzing the capability description of testing institution in Chinese phrase using a joint approach of semi-supervised K-Means clustering and BERT.

IF 3.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Gaoqing Xu, Qun Chen, Shuhang Jiang, Xiaohang Fu, Yiwei Wang, Qingchun Jiao
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引用次数: 0

Abstract

The capability parameters of third-party testing institutions not only serve as a critical reflection of their technical and quality management capabilities but also form the key basis for categorizing their testing abilities. However, current Chinese phrase-based descriptions of these capability parameters are influenced by diverse expression styles and varying internal standards, making it difficult to establish consistent criteria for classifying testing capabilities. This inconsistency presents notable difficulties for clients and regulatory bodies. Therefore, leveraging clustering techniques to uncover the intrinsic relationships and latent information between testing capabilities and their corresponding parameters is one of the crucial approaches to achieving scientific and reasonable classification of testing capabilities. Traditional text feature extraction methods suffer from several limitations, including sparse features, high-dimensional features, and lack of semantic information. These shortcomings complicate the classification and analysis of testing capability descriptions. To address this issue, this study focuses on the "products and testing objects" within the capability parameters of Chinese testing institutions as the research subject and proposes a joint model based on BERT and semi-supervised K-Means clustering. This model employs BERT to extract textual features from Chinese descriptive phrases and combines them with a small number of labeled samples for semi-supervised K-Means clustering analysis. The clustering results are then used to train a multi-output Softmax classifier, thereby enabling the classification of testing capabilities for third-party institutions. Experimental results demonstrate that the proposed method outperforms traditional methods such as TF-IDF and one-hot encoding when applied to the Chinese description datasets of testing institutions. Specifically, it exhibits advantages in reducing the dimensionality of textual features and enhancing clustering performance. When the proportion of labeled samples accounts for 10% of the total sample size, the method achieves optimal clustering results, with an average classifier accuracy of 89.8%.

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来源期刊
Scientific Reports
Scientific Reports Natural Science Disciplines-
CiteScore
7.50
自引率
4.30%
发文量
19567
审稿时长
3.9 months
期刊介绍: We publish original research from all areas of the natural sciences, psychology, medicine and engineering. You can learn more about what we publish by browsing our specific scientific subject areas below or explore Scientific Reports by browsing all articles and collections. Scientific Reports has a 2-year impact factor: 4.380 (2021), and is the 6th most-cited journal in the world, with more than 540,000 citations in 2020 (Clarivate Analytics, 2021). •Engineering Engineering covers all aspects of engineering, technology, and applied science. It plays a crucial role in the development of technologies to address some of the world''s biggest challenges, helping to save lives and improve the way we live. •Physical sciences Physical sciences are those academic disciplines that aim to uncover the underlying laws of nature — often written in the language of mathematics. It is a collective term for areas of study including astronomy, chemistry, materials science and physics. •Earth and environmental sciences Earth and environmental sciences cover all aspects of Earth and planetary science and broadly encompass solid Earth processes, surface and atmospheric dynamics, Earth system history, climate and climate change, marine and freshwater systems, and ecology. It also considers the interactions between humans and these systems. •Biological sciences Biological sciences encompass all the divisions of natural sciences examining various aspects of vital processes. The concept includes anatomy, physiology, cell biology, biochemistry and biophysics, and covers all organisms from microorganisms, animals to plants. •Health sciences The health sciences study health, disease and healthcare. This field of study aims to develop knowledge, interventions and technology for use in healthcare to improve the treatment of patients.
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