Search Results (6)

The assessment of Chinese text readability plays a significant role in Chinese language education. Due to the intrinsic differences between alphabetic languages and Chinese character representations, the readability assessment becomes more challenging in terms of the language’s inherent complexity in vocabulary, syntax, and semantics. The article proposed the conceptual analogy between Chinese readability assessment and music’s rhythm and tempo patterns, in which the syntactic structures of the Chinese sentences could be transformed into an image. The Chinese Knowledge and Information Processing Tagger (CkipTagger) tool developed by Sinica-Taiwan is utilized to decompose the Chinese text into a set of tokens. These tokens are then refined through a user-defined token pool to retain meaningful units. An image with part-of-speech (POS) information will be generated by using the token versus syntax alignment. A discrete cosine transform (DCT) is then applied to extract the temporal characteristics of the text. Moreover, the study integrated four categories: linguistic features–type–token ratio, average sentence length, total word, and difficulty level of vocabulary for the readability assessment. Finally, these features were fed into the Support Vector Machine (SVM) network for the classifications. Furthermore, a bidirectional long short-term memory (Bi-LSTM) network is adopted for quantitative comparisons. In simulation, a total of 774 Chinese texts fitted with Taiwan Benchmarks for the Chinese Language were selected and graded by Chinese language experts, consisting of equal amounts of basic, intermediate, and advanced levels. The finding indicated the proposed POS with the linguistic features work well in the SVM network, and the performance matches with the more complex architectures like the Bi-LSTM network in Chinese readability assessments. Full article

Ancient Chinese scripts—including oracle bone carvings, bronze inscriptions, stone steles, Dunhuang scrolls, and bamboo slips—are rich in historical value but often degraded due to centuries of erosion, damage, and stylistic variability. These issues severely hinder manual transcription and render conventional OCR techniques inadequate, as they are typically trained on modern printed or handwritten text and lack interpretability. To tackle these challenges, we propose FAIR-Net, a hybrid architecture that combines the unsupervised feature learning capacity of a deep autoencoder with the semantic transparency of a fuzzy rule-based classifier. In FAIR-Net, the deep autoencoder first compresses high-resolution character images into low-dimensional, noise-robust embeddings. These embeddings are then passed into a Fuzzy Neural Network (FNN), whose hidden layer leverages Fuzzy C-Means (FCM) clustering to model soft membership degrees and generate human-readable fuzzy rules. The output layer uses Iteratively Reweighted Least Squares Estimation (IRLSE) combined with a Softmax function to produce probabilistic predictions, with all weights constrained as linear mappings to maintain model transparency. We evaluate FAIR-Net on CASIA-HWDB1.0, HWDB1.1, and ICDAR 2013 CompetitionDB, where it achieves a recognition accuracy of 97.91%, significantly outperforming baseline CNNs (p < 0.01, Cohen’s d > 0.8) while maintaining the tightest confidence interval (96.88–98.94%) and lowest standard deviation (±1.03%). Additionally, FAIR-Net reduces inference time to 25 s, improving processing efficiency by 41.9% over AlexNet and up to 98.9% over CNN-Fujitsu, while preserving >97.5% accuracy across evaluations. To further assess generalization to historical scripts, FAIR-Net was tested on the Ancient Chinese Character Dataset (9233 classes; 979,907 images), achieving 83.25% accuracy—slightly higher than ResNet101 but 2.49% lower than SwinT-v2-small—while reducing training time by over 5.5× compared to transformer-based baselines. Fuzzy rule visualization confirms enhanced robustness to glyph ambiguities and erosion. Overall, FAIR-Net provides a practical, interpretable, and highly efficient solution for the digitization and preservation of ancient Chinese character corpora. Full article

Speech recognition technology is an important branch in the field of artificial intelligence, aiming to transform human speech into computer-readable text information. However, speech recognition technology still faces many challenges, such as noise interference, and accent and speech rate differences. An aim of this paper is to explore a deep learning-based speech recognition method to improve the accuracy and robustness of speech recognition. Firstly, this paper introduces the basic principles of speech recognition and existing mainstream technologies, and then focuses on the deep learning-based speech recognition method. Through comparative experiments, it is found that the self-attention mechanism performs best in speech recognition tasks. In order to further improve speech recognition performance, this paper proposes a deep learning model based on the self-attention mechanism with DCNN-GRU. The model realizes the dynamic attention to an input speech by introducing the self-attention mechanism in a neural network model instead of an RNN and with a deep convolutional neural network, which improves the robustness and recognition accuracy of this model. This experiment uses 170 h of Chinese dataset AISHELL-1. Compared with the deep convolutional neural network, the deep learning model based on the self-attention mechanism with DCNN-GRU accomplishes a reduction of at least 6% in CER. Compared with a bidirectional gated recurrent neural network, the deep learning model based on the self-attention mechanism with DCNN-GRU accomplishes a reduction of 0.7% in CER. And finally, this experiment is performed on a test set analyzed the influencing factors affecting the CER. The experimental results show that this model exhibits good performance in various noise environments and accent conditions. Full article

In the German translation history of the Daodejing, the version rendered by the renowned German sinologist, Richard Wilhelm, has vigorously propelled the study of Laozegetics in Germany and stands as a translation of historical and scholarly significance. Wilhelm complemented the concise main text through the use of diverse, precise, and appropriate paratexts, granting his translation both readability and academic rigor. This ensures the admiration of general readers and the recognition of professional scholars. Tailored to the linguistic preferences and educational levels of German readers, Wilhelm frequently employed highly recognizable theological, philosophical, and literary concepts within the German cultural system to elucidate the Daodejing. This translation strategy effectively satisfies the expectation horizon of target readers. In the paratexts, Wilhelm constructs a philosophical framework of Daoism, compares the thought of Confucianism and Daoism, and broadens the dialogue between Chinese philosophical thought and Western intellectual traditions, thereby bestowing upon the Daodejing a renewed vitality in the German-speaking world. Full article

This study examines the impact of environmental information disclosure quality on firm value for Chinese listed companies in heavily polluting industries from 2010 to 2021. By controlling for the level of leverage, growth, and corporate governance, a fixed effects model is constructed to test this relationship. Furthermore, this study analyzes the moderating effects of annual report text features, such as length, similarity, and readability, on the relationship between environmental information disclosure and firm value and the heterogeneous impact of firm ownership on this relationship. The main findings of this study are as follows: There is a positive correlation between the level of environmental information disclosure and firm value for Chinese listed companies in heavily polluting industries. Annual report text length and readability positively moderate the relationship between environmental information disclosure and firm value. Annual report text similarity negatively moderates the relationship between environmental information disclosure and firm value performance. Compared with state-owned enterprises, the impact of environmental information disclosure quality on the firm value of no-state-owned enterprises is more significant. Full article

The present article continues the modern scholarship on the transmission of Buddhism from India to China by focusing on one of the most influential figures among the early Chinese Buddhist translators, namely, Zhi Qian (支謙, ca. 193–252 CE). His translation style is characterized as “kaleidoscopic,” as Jan Nattier describes, due to the high degree of diversity and variability in his language and terminology. In this study, we explore Zhi Qian’s literary refinements from the lexical, stylistic, and conceptual points of view based on his Foshuo pusa benye jing (佛說菩薩本業經, T. 281) in close conjunction with three related sūtras, the Foshuo dousha jing (佛説兜沙經, T. 280), the Zhu pusa qiufo benye jing (諸菩薩求佛本業經, T. 282), and the Pusa shizhu xingdao pin (菩薩十住行道品, T. 283), all attributed to Lokakṣema. We specifically discuss how Zhi Qian produced such a polished and “sinicised” version with various modes of literary modifications (e.g., using wenyan elements, four-syllable prosodic pattern, diverse vocabulary, and indigenous Chinese concepts) within the context of his life and times. In this article, we also argue that his main aim in producing the Foshuo pusa benye jing was to provide a more classical, elegant, and readable Buddhist scripture to the Chinese readers, but that he had to sacrifice being able to faithfully reflect the language used in the original Indic texts. Full article