Chinese Text Research Articles

An Eco-Translatological Study of the English Version of Xi Jinping: The Governance of China IV

With the rapid development of China’s foreign relations, the effective dissemination of external publicity texts through high-quality translations has become a pivotal task in enhancing communication between China and the world. Xi Jinping: The Governance of China IV compiles President Xi Jinping’s speeches, talks, addresses, instructions, and congratulatory letters during his presidency, offering significant insights into Chinese political culture. This paper employs Eco-translatology to identify representative cases from the book and analyze the application of translation principles, strategies, and techniques in translating Chinese political texts. Specifically, the study examines translators’ adaptation and selection practices. It investigates the three-dimensional transformations of language, culture, and communication within the English rendition of Xi Jinping: The Governance of China IV.

Sentiment analysis of Chinese ancient poetry based on multidimensional knowledge attention

Abstract Poetry was a unique literary genre in ancient China as an important way to express sentiments. Chinese ancient poetry not only has simple words, strict meters, and rich semantic relationships, but also widely use rhetorical techniques such as simile and personification, as well as metaphorical means such as allusion and imagery, which makes it difficult to understand their implicit sentiments quickly and accurately. Therefore, this article attempts to make full use of the semantic features of Chinese ancient poetry text and the knowledge feature of related domains under the guidance of the research paradigm of digital intelligence integration and, based on which, proposed Sentiment Analysis Model of Chinese Ancient Poetry based on Multidimensional Knowledge Attention. This model extracts semantic features from Chinese ancient poetry text, meanwhile designs a multidimensional knowledge attention to extract knowledge features from the knowledge base of Chinese ancient poetry. The recognition of Chinese ancient poetry sentiment is performed by integrating the textual feature and knowledge feature. Comparison experiments on the open ancient poetry corpus verified the effectiveness of the proposed model, and the ablation experiment explored the importance of different knowledge to the sentiment analysis result of Chinese ancient poetry.

A Chinese named entity recognition method for landslide geological disasters based on deep learning

Landslide Named Entity Recognition (LNER) involves extracting specific entities from Chinese unstructured landslide disaster texts, which is crucial for constructing a knowledge graph and supporting landslide prevention efforts. This study proposes a deep learning-based LNER model that utilizes Bidirectional Encoder Representations from Transformer (BERT) for word embeddings and integrates the Conditional Random Fields (CRF) algorithm and projected gradient descent (PGD) adversarial neural networks to enhance sequence labeling accuracy. The practical implications of this research lie in improving the efficiency and precision of disaster information extraction, aiding in real-time decision-making and risk mitigation strategies. Experiments on the constructed dataset show that the model effectively identifies eight types of landslide entities, achieving a highest F1 score of 89.7%.

Comparative analysis of the image of a Chinese migrant in the Chinese mainland media and the media of Chinese emigration

The research presents a comparative analysis of the image of a Chinese migrant in the mainland Chinese media and the media of Chinese emigration. The research is based on the study of the content of publications such as The People's Daily and Xinhua on the one hand, and emigrant media, including The World Journal and The Epoch Times, on the other. The aim of the study is to identify differences and similarities in the representation of Chinese migrants, as well as to assess the influence of the political and cultural context on the formation of their image. In the Chinese mainland media, migrants are often presented as successful and patriotic representatives of the nation, keeping in touch with their homeland, which corresponds to China's "soft power" strategy. The expatriate opposition media is also biased in shaping the image of the Chinese migrant, often labeling migration as political rather than economic. The object of the study is the materials of the Chinese mainland and migrant media on the issues of Chinese emigration in the period 2023-2024. The subject of the study is the image of a Chinese migrant in the Chinese media. The methodology of this study includes content analysis and quantitative analysis of Chinese news media texts. As a result of the conducted research, the main similarities and significant differences in the representation of Chinese migrants in mainland Chinese media and emigrant media were revealed. The image of a migrant in the Chinese mainland media is formed within the framework of state propaganda, with an emphasis on patriotism and the preservation of cultural ties with the Motherland. Migrants are portrayed as successful representatives of the Chinese nation, keeping in touch with China and promoting its interests abroad. This reflects China's state policy of strengthening "soft power" and supporting the diaspora for economic and cultural influence in the international arena. The expatriate opposition media are also biased in shaping the image of the Chinese migrant, often labeling migration as political rather than economic. On the other hand, independent expatriate media such as the World Journal reflect more diverse and often critical points of view, highlighting the problems of migrant integration, discrimination and the complexities of life in another country. Independence from the Chinese authorities allows these media to focus on practical issues, avoiding political bias.

The propagation paths of the Catholic doctrine of the Trinity in the Ming and Qing dynasties

According to historical analysis and textual interpretation, the propagation of the Catholic doctrine of the Trinity in the Ming and Qing dynasties was closely related to Matteo Ricci. When entering China, Ricci put forward the ‘Theory of Buddhist-Daoist Plagiarism’, asserting that Buddhism and Taoism had plagiarised the Catholic doctrine on the triune God. As for Confucianism, he pointed out that the ancient Confucian classics did not contain the doctrine of the Trinity, which he attributed to various facters, leading to what may be termed the ‘Confucian Absence Theory’. Matteo Ricci’s interpretations and propositions regarding the Chinese Trinity paved the way for the ‘Yi-Fo School’, the ‘Bu-Ru School’ and the Figurism, which emerged during the dissemination of the Trinity in the Ming and Qing dynasties. The ‘Yi-Fo School’ advocated replacing the Buddhist Trinity with the Catholic Trinity to complement the perceived absence in Confucianism, thus forming the ‘Bu-Ru School’. In contrast, Figurism advocated for use of Chinese characters and texts as clues to uncover the missing Trinity in Confucian classics. Regarding Matteo Ricci’s viewpoints and the above three paths, Buddhism of that era believed that Catholicism had plagiarised the Buddhist doctrine of the Trinity, and modern Chinese scholars also argued that the views of the Figurism were overly far-fetched. These paths placed the Trinitarian doctrines of Catholicism, Buddhism and Daoism in a cross-linguistic and cross-cultural comparative framework, underscoring the methodological significance of exchanges and mutual learning among civilisations.Contribution: This article examines the nexus between Matteo Ricci and the propagation of the Catholic doctrine of the Trinity in the Ming and Qing dynasties. It also investigates the evolution of three distinct pathway for this doctrinal propagation and assesses the Chinese Populace’s reactions, as well as the interplay of their discourses.

A hybrid BiLSTM-CNN deep learning model for Chinese sentiment analysis of online car reviews

Although sentiment analysis with datasets in English has achieved significant progress, there are still relatively few studies on sentiment analysis in the area of Chinesecar review texts. In addition, the existing Chinese text sentiment analysis methods cannot simultaneously extract the contextual features and the local semantic information from the review texts. To address the above issues, a hybrid deep learning model namely BiLSTM-CNN was designed and validated with Chinese car review texts. We trained word vectors with the CBOW model by combining the Chinese Wikipedia corpus with other open-source car-related corpora. Such word vectors include car-specific vocabulary, which improves sentimental classification accuracy. Experimental results show that the performance indices of the deep learning models (CNN, LSTM, BiLSTM) are much better than the KNN, SVM, Naïve Bayes, and RF model, which is attributed to deep learning's powerful feature extraction ability and nonlinear fitting ability. Furthermore, when compared to deep learning models such as CNN, LSTM, BiLSTM, and LSTM-CNN, the suggested hybrid BiLSTM-CNN model outperformed the others. The results may provide references for consumers to buy a car and for car companies to optimize their products.

Sentiment Analysis of Chinese Ancient Poetry Based on Multidimensional Attention Under the Background of Digital Intelligence Integration

Abstract Poetry was a unique literary genre in ancient China as an important way to express sentiments. Chinese ancient poetry not only has simple words, strict meters and rich semantic relationships, but also widely uses rhetorical techniques such as simile and personification, as well as metaphorical means such as allusion and imagery, which makes it difficult to understand their implicit sentiments quickly and accurately. Therefore, this paper attempts to make full use of the semantic features of Chinese ancient poetry text and the knowledge feature of related domains under the guidance of the research paradigm of digital intelligence integration, and based on which, proposed Sentiment Analysis Model of Chinese Ancient Poetry based on Multidimensional Attention (SAMCAP). This model extracts semantic features from Chinese ancient poetry text, meanwhile designs a multidimensional attention to extract the knowledge feature from the knowledge base of Chinese ancient poetry. The recognition of Chinese ancient poetry sentiment is performed by integrating the textual feature and knowledge feature. Comparison experiments on the open ancient poetry corpus verified the effectiveness of the proposed model, and the ablation experiment explored the importance of different knowledge to the sentiment analysis result of Chinese ancient poetry.

Multimodal Seed Data Augmentation for Low-Resource Audio Latin Cuengh Language

Latin Cuengh is a low-resource dialect that is prevalent in select ethnic minority regions in China. This language presents unique challenges for intelligent research and preservation efforts, primarily due to its oral tradition and the limited availability of textual resources. Prior research has sought to bolster intelligent processing capabilities with regard to Latin Cuengh through data augmentation techniques leveraging scarce textual data, with modest success. In this study, we introduce an innovative multimodal seed data augmentation model designed to significantly enhance the intelligent recognition and comprehension of this dialect. After supplementing the pre-trained model with extensive speech data, we fine-tune its performance with a modest corpus of multilingual textual seed data, employing both Latin Cuengh and Chinese texts as bilingual seed data to enrich its multilingual properties. We then refine its parameters through a variety of downstream tasks. The proposed model achieves a commendable performance across both multi-classification and binary classification tasks, with its average accuracy and F1 measure increasing by more than 3%. Moreover, the model’s training efficiency is substantially ameliorated through strategic seed data augmentation. Our research provides insights into the informatization of low-resource languages and contributes to their dissemination and preservation.

FLCMC: Federated Learning Approach for Chinese Medicinal Text Classification.

Addressing the privacy protection and data sharing issues in Chinese medical texts, this paper introduces a federated learning approach named FLCMC for Chinese medical text classification. The paper first discusses the data heterogeneity issue in federated language modeling. Then, it proposes two perturbed federated learning algorithms, FedPA and FedPAP, based on the self-attention mechanism. In these algorithms, the self-attention mechanism is incorporated within the model aggregation module, while a perturbation term, which measures the differences between the client and the server, is added to the local update module along with a customized PAdam optimizer. Secondly, to enable a fair comparison of algorithms' performance, existing federated algorithms are improved by integrating a customized Adam optimizer. Through experiments, this paper first conducts experimental analyses on hyperparameters, data heterogeneity, and validity on synthetic datasets, which proves that the proposed federated learning algorithm has significant advantages in classification performance and convergence stability when dealing with heterogeneous data. Then, the algorithm is applied to Chinese medical text datasets to verify its effectiveness on real datasets. The comparative analysis of algorithm performance and communication efficiency shows that the algorithm exhibits strong generalization ability on deep learning models for Chinese medical texts. As for the synthetic dataset, upon comparing with comparison algorithms FedAvg, FedProx, FedAtt, and their improved versions, the experimental results show that for data with general heterogeneity, both FedPA and FedPAP show significantly more accurate and stable convergence behavior. On the real Chinese medical dataset of doctor-patient conversations, IMCS-V2, with logistic regression and long short-term memory network as training models, the experiment results show that in comparison to the above three comparison algorithms and their improved versions, FedPA and FedPAP both possess the best accuracy performance and display significantly more stable and accurate convergence behavior, proving that the method in this paper has better classification effects for Chinese medical texts.

From Text to Safety: A Novel Framework for Mining Unsafe Aviation Events Using Advanced Neural Network and Feature Network

The rapid growth of the aviation industry highlights the need for strong safety management. Analyzing data on unsafe aviation events is crucial for preventing risks. This paper presents a new method that integrates the Transformer network model, clustering analysis, and feature network modeling to analyze Chinese text data on unsafe aviation events. Initially, the Transformer model is used to generate summaries of event texts, and the performance of three pre-trained Chinese models is evaluated and compared. Next, the Jieba tool is applied to segment both summarized and original texts to extract key features of unsafe events and prove the effectiveness of the pre-trained Transformer model in simplifying lengthy and redundant original texts. Then, cluster analysis based on text similarity categorizes the extracted features. By solving the correlation matrix of these features, this paper constructs a feature network for unsafe aviation events. The network’s global and individual metrics are calculated and then used to identify key feature nodes, which alert aviation professionals to focus more on the decision-making process for safety management. Based on the established network and these metrics, a data-driven hidden danger warning strategy is proposed and illustrated. Overall, the proposed method can effectively analyze Chinese texts of unsafe aviation events and provide a basis for improving aviation safety management.

CJE-PCHF: Chinese Joint Entity and Relation Extraction Model Based on Progressive Contrastive Learning and Heterogeneous Feature Fusion

The joint extraction of entities and relations is a critical task in information extraction, and its performance directly affects the performance of downstream tasks. However, existing joint extraction models based on deep learning exhibit weak processing capabilities for the phenomenon of multiple pronunciations of one character and multiple characters of one pronunciation when processing Chinese texts, resulting in a performance loss. To address these issues, this paper introduces part-of-speech (POS) and pinyin features to aid the model in learning semantic features that are more contextually appropriate. We propose a Chinese Joint Entity and Relation Extraction Model based on progressive contrastive learning and heterogeneous feature fusion (CJE-PCHF). During model training, an interactive fusion network based on progressive contrastive learning is employed to learn the dependencies between pinyin, POS, and semantic features. This guides the model in heterogeneous feature fusion, capturing higher-order semantic associations between heterogeneous features. On the commonly used DuIE evaluation dataset for joint extraction, our model achieved a significant improvement, with the F1 score increasing by 5.4% compared to the benchmark model CasRel.

The Path Driving China’s Energy Structure Transformation from the Perspective of Policy Tools

Combing through Chinese energy-related policy texts and exploring the development path of energy restructuring are significant steps towards a better understanding of the history of energy restructuring in the process of building a moderately prosperous society in all aspects. To explore the various paths driving the transformation of China’s energy structure, the energy policies promulgated at the national level from 2001 to 2020 in China were systematically compiled. Based on the policy tool perspective, a theoretical framework for influencing China’s energy structure transition was proposed in three dimensions: objectives, tools, and intensity. A total of 39 national energy policy texts were analyzed using a fuzzy set qualitative comparative analysis method. The results show that (1) the consistency levels of individual preconditions were all below 0.9, which does not constitute a necessary condition for the transformation of China’s energy structure; (2) the sufficiency analysis identified three paths of China’s energy structure transformation, which can be described as models driven by “multiple objectives + information”, “single objective + information”, and “single objective”; (3) energy restructuring is a goal-oriented process; the clarity and certainty of the efficient and green objectives of energy policies have a facilitating effect on energy restructuring, and the support of information technology provides an important guarantee for energy restructuring; (4) compared with European and American countries, it was found that China’s energy policy tools were characterized by an emphasis on macro-planning, insufficient connectivity, and marked tendencies in practice.

Named Entity Recognition for Equipment Fault Diagnosis Based on RoBERTa-wwm-ext and Deep Learning Integration

Equipment fault diagnosis NER is to extract specific entities from Chinese equipment fault diagnosis text, which is the premise of constructing an equipment fault diagnosis knowledge graph. Named entity recognition for equipment fault diagnosis can also provide important data support for equipment maintenance support. Equipment fault diagnosis text has complex semantics, fuzzy entity boundaries, and limited data size. In order to extract entities from the equipment fault diagnosis text, this paper presents an NER model for equipment fault diagnosis based on RoBERTa-wwm-ext and Deep Learning network integration. Firstly, this model uses the RoBERTa-wwm-ext to extract context-sensitive embeddings of text sequences. Secondly, the context feature information is obtained through the BiLSTM network. Thirdly, the CRF is combined to output the label sequence with a constraint relationship, improve the accuracy of sequence labeling task, and complete the entity recognition task. Finally, experiments and predictions are carried out on the constructed dataset. The results show that the model can effectively identify five types of equipment fault diagnosis entities and has higher evaluation indexes than the traditional model. Its precision, recall, and F1 value are 94.57%, 95.39%, and 94.98%, respectively. The case study proves that the model can accurately recognize the entity of the input text.

Key role of PPAR-γ-mediated suppression of the NFκB signaling pathway in rutin's antidepressant effect

BackgroundDepression is a chronic and recurrent disorder with an unknown etiology. Efficacious antidepressant treatments with minimal side effects are urgently needed. Neuroinflammation may contribute to depression, as anti-inflammatory drugs have been shown to alleviate depressive symptoms in clinical practice. Rutin, a naturally occurring flavonoid derived from plants, is abundant in many antidepressant herbs, including Hemerocallis citrina Baroni. Historical Chinese medical texts, including the renowned Compendium of Materia Medica, document H. citrina Baroni as possessing antidepressant properties. Rutin, one of its primary active constituents, is recognized for its anti-inflammatory effects. Despite this, little is known about its specific target and mechanism. MethodsIn the present study, molecular docking, and surface plasmon resonance imaging (SPRi) analysis were used to identify the special targets of rutin. Meanwhile, the potential antidepressant effects were evaluated in the chronic social defeat stress (CSDS) paradigm, an animal model of depression. Then, Western blotting, quantitative real-time polymerase chain reaction (qRT-PCR), Co-immunoprecipitation (Co-IP) as well as antagonists of PPAR-γ were utilized to investigate the mechanism underlying the antidepressant effect of rutin. ResultsBoth molecular docking and SPRi analysis showed high docking scores and interactions between rutin and PPAR-γ. In vivo, rutin promoted the nuclear translocation of PPAR-γ in the hippocampus of mice, inhibited NFκB-mediated inflammatory pathways, and subsequently reduced the expression of pro-inflammatory factors (e.g., iNOS, IL-6), aligning with an antidepressant effect. However, this therapeutic effect was attenuated by GW9662, a specific antagonist of PPAR-γ. ConclusionAs a result of activating PPAR-γ and inhibiting NFκB pathway activation, rutin reduces neuroinflammation and exhibits an antidepressant effect. These findings shed light on the antidepressant mechanism of rutin and could be valuable for the development of new antidepressants.

Digital Archives of Buddhist Cultural Heritage on the Eastern Silk Road: Status and Utilization in Dunhuang, Turfan, and Kucha

The advancement of digital humanities in the 21st century has significantly enhanced the importance of digital archives in Buddhist art research. The digitization of the extensive and geographically diverse Buddhist cultural heritage along the eastern Silk Road has greatly improved the efficiency and accessibility of scholarly research. This paper examines seminal digital archives initiatives pertaining to the region's Buddhist legacy.Notable examples analyzed in this study include the International Dunhuang Project (IDP); the Database of Medieval Chinese Texts, a collaborative effort between Belgium's Ghent University Centre for Buddhist Studies and Taiwan's Chung-Hwa Institute of Buddhist Studies; the Digital Silk Road projects at Japan's Tōyō Bunko and Ryukoku University; the Dunhuang Academy's Dunhuang Documents Database and Digital Dunhuang platform; and Germany's Turfan Archive and Buddhist Murals of Kucha Archive. These digital archives offer access to high-resolution images and rare materials, encompassing Buddhist scriptures, Dunhuang manuscripts, paintings, and sculptures.These digital archives are crucial for art historical research, contributing to the understanding of original contexts, meanings, and religious functions through the virtual reunification of dispersed artworks. Moreover, these digital archives transcend time and space constraints, facilitating comprehensive research, aiding in cultural heritage preservation, and increasing public accessibility. As these digital repositories continue to expand and interconnect, they are poised to deepen our understanding of Silk Road Buddhist art, paving the way for innovative research approaches and promoting international scholarly cooperation.

Comparative Analysis of Large Language Models in Chinese Medical Named Entity Recognition.

The emergence of large language models (LLMs) has provided robust support for application tasks across various domains, such as name entity recognition (NER) in the general domain. However, due to the particularity of the medical domain, the research on understanding and improving the effectiveness of LLMs on biomedical named entity recognition (BNER) tasks remains relatively limited, especially in the context of Chinese text. In this study, we extensively evaluate several typical LLMs, including ChatGLM2-6B, GLM-130B, GPT-3.5, and GPT-4, on the Chinese BNER task by leveraging a real-world Chinese electronic medical record (EMR) dataset and a public dataset. The experimental results demonstrate the promising yet limited performance of LLMs with zero-shot and few-shot prompt designs for Chinese BNER tasks. More importantly, instruction fine-tuning significantly enhances the performance of LLMs. The fine-tuned offline ChatGLM2-6B surpassed the performance of the task-specific model BiLSTM+CRF (BC) on the real-world dataset. The best fine-tuned model, GPT-3.5, outperforms all other LLMs on the publicly available CCKS2017 dataset, even surpassing half of the baselines; however, it still remains challenging for it to surpass the state-of-the-art task-specific models, i.e., Dictionary-guided Attention Network (DGAN). To our knowledge, this study is the first attempt to evaluate the performance of LLMs on Chinese BNER tasks, which emphasizes the prospective and transformative implications of utilizing LLMs on Chinese BNER tasks. Furthermore, we summarize our findings into a set of actionable guidelines for future researchers on how to effectively leverage LLMs to become experts in specific tasks.

Crossing boundaries in an age of post-translation studies

Abstract The cycle of symphonic songs Das Lied von der Erde was based on Die Chinesische Flöte, a German version of classical Chinese poems. As a result, Die Chinesische Flöte crossed the boundary between literature and music, encompassing intralingual, interlingual, and intersemiotic translations. While comparative textual analysis reveals that these two works made changes to the original Chinese texts, intersemiotic translation illustrates that the borders between verbal texts and nonverbal works are fluid. This post-translation study traces the translation context of Das Lied, maps Die Chinesische Flöte’s transitions between literary and musical polysystems, and traces Das Lied’s afterlife in the creation of new artworks. It concludes that poetry, music, and other art forms can mutually translate, complement, and become afterlives of one another.

Tracing Political Implications of the Color Red in China From 1919 to 1976

This study explores the political implications of the color red in China from 1919 to 1976, analyzing its usage and significance within published literature through the lens of historical events and ideological shifts. Utilizing the Google Book Ngram Viewer and Voyant tools, this research traces patterns in the frequency and context of the term "红色" (red) in Chinese texts. The analysis identifies significant peaks in usage corresponding to key historical moments, including the Nanchang Uprising, the Marco Polo Bridge Incident, the establishment of the People's Republic of China, and the Cultural Revolution. The study finds that the color red is uniquely intertwined with political discourse in China, contrasting with other politically charged colors such as black, white, and yellow. Despite the color’s symbolic prominence, its direct references in pivotal political texts like Mao Zedong’s Quotations are relatively sparse, suggesting a complex interplay between visual symbolism and textual representation. The findings highlight the color red’s profound role in reflecting and shaping political ideologies during this transformative period in Chinese history.

Chartalism in Ancient China: A Retrospective of Monetary Thought

Abstract Chartalism is an influential and continuous tradition of monetary thought in ancient China. It dates back to the earliest Chinese text on monetary thought and continued into the Qing Dynasty. A vast body of related literature can be found across ancient Chinese dynasties. Chartalism in ancient China had the following main propositions. First, money originated as an invention of ancient monarchs and is a creature of the state. The second is the nominal theory of money: useless metal can function as money and become a tool of state intervention in the economy through state support. Third, the right to issue money should be monopolized by the state; otherwise, the state's monetary policy will be interfered with by other currencies. Fourth, an important monetary policy is the government's buying and selling of commodities countercyclically by issuing and receiving money, thus stabilizing prices. Fifth, the circulation process of sovereign money consists of two parts: the state's minting of coins (or issuing of paper money) and spending are the outflow of money, and levying monetary taxes (or selling goods) is the inflow of money. The state stabilizes the value of money by intervening in the circulation process.

Transformer-Based Video Comment Analysis

In this work, we conduct a comprehensive analysis of sentiment in Bilibili comments using a Transformer-based model. We employ the mT5_m2o_chinese_simplified_crossSum model, a multitasking Transformer model specifically designed for summarizing Chinese text. The study involves scraping comments from a video related to the "trolley problem," followed by data cleaning and preprocessing. The preprocessed data is fed into the Transformer model, which generates summaries that accurately reflect the main viewpoints and discussions in the comments. Our results show that the model effectively captures key ethical dilemmas, personal opinions, and emotional responses, providing a nuanced understanding of public sentiment. This research highlights the potential of advanced NLP techniques in processing large volumes of user-generated content, offering valuable insights for businesses and researchers in understanding public opinion and facilitating ethical discussions. The study underscores the importance of optimizing Transformer models for specific tasks, demonstrating their flexibility and performance in text summarization. These findings provide a robust foundation for future applications and innovations in natural language processing.